System and method producing high definition video from low definition video

ABSTRACT

Provided is system and a method of producing a high definition video from a low definition video. According to one or more embodiments, the method performs a trajectory estimation and a matching operation only with respect to a feature region pixel of a previous frame of the low definition video, which is different from all pixels of the previous frame, thereby reducing an amount of computation to improve efficiency of a memory, producing the high definition video in real time, and producing a more accurate high definition video with respect to a feature region.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Korean Patent Application No.10-2009-0089503, filed on Sep. 22, 2009, in the Korean IntellectualProperty Office, the disclosure of which is incorporated herein byreference.

BACKGROUND

1. Field

One or more embodiments relate to a system and method of producing ahigh definition video from a low definition video, and moreparticularly, to a system and method of producing a high definitionvideo using trajectory estimation and interpolation.

2. Description of the Related Art

Image magnification of a low definition video to a high definition videomay be an important function of image equipment, and may be an equallyimportant element that is extensively applicable in everyday lifeproducts such as camcorders, or in professional fields such asastronomy, health care, and the like. Recently, there have been anincreased number of instances where image magnification have beenapplied, such as when converting a low definition video, obtained from adigital camera or a camcorder, into a high definition video, reproducinglow definition video contents in a high definition display apparatus, orcompressing a high definition video into a low definition video and thenrestoring the compressed low definition video to the high definitionvideo. Accordingly, there may be an increase in a desire for imagemagnification to obtain high definition video from low definition video.

As one example, image magnification may be implemented using acombination of lenses. However, when magnification is carried out forhigh magnification using such an optical magnification scheme, thisentails a significant increase in volume, weight, cost, and the like. Toovercome these problems, image magnification schemes using digital imageprocessing may be used. When using such digital image processing, theimage magnification may be desirably implemented using a semiconductorchip, and thereby represent a relatively low cost in comparison with theoptical magnification scheme. In addition, when the image magnificationis used in conjunction with an existing optical magnification scheme, animage that is more effectively magnified at a high magnification may beobtained. Also, in terms of signal processing, the image magnificationscheme may be used to maintain compatibility between systems usingimages that are different from one another by orders of magnitude, aswell as being used to increase a resolution or magnitude of an image.The image magnification scheme may be applicable in reducing the amountof data required for expressing an image in association with imagecompression.

In terms of the signal processing or image processing, the imagemagnification scheme may have several approaches. In one approach, animage magnification scheme using interpolation of signals may be easilyimplemented and practically used. The interpolation of images may be ascheme that may expand intervals between discrete pixels of an originalimage, and estimate values between the expanded intervals to insert theestimated value into the expanded intervals, thereby obtaining effectsin magnifying the image. In this instance, an image magnification methodmay be determined depending on a manner in which the values between theexpanded intervals are estimated, and the magnified image may varydepending on the determined image magnification method.

For example, FIG. 14 illustrates a conventional image processing systemwhere plural frames of a video sequence are input to the multi-framememory, and more specifically a separate frame memory is required foreach frame, e.g., when interpolating pixels within a current frame basedon movement estimation from 10 previous frames 10 separate framememories are required in the multi-frame memory. The multi-framemovement estimation unit 12 estimates movement for each pixel or blockof a current frame in relation to the corresponding pixel or block fromall previous frames. For example, movement estimation of each pixel maybe based on at least 10 previous frames, requiring 10 separate movementestimations based on each of the previous frames for each of the pixelsor blocks. From this review of all the previous frames for each pixel orblock, an estimation of movement for each pixel or block can beestimated, such that the high resolution interpolation unit 14 canperform an interpolation of pixels or blocks for a current frame.

However, such an approach also requires substantial memory andprocessing resources, and accordingly results in increased costs, delay,and a limitation of the image conversion to being done in a moresubstantial memory and processing environment. As noted, a separateframe memory is required for each previous frame, and every pixel orblock of the previous frames are stored in each separate frame memory.Further, as movement estimation is required for every pixel or block ofa current frame for every previous frame relied upon for movementestimation, substantial processing is required which also requiressubstantial time. Accordingly, due to such drawbacks, such an imageconversion approach cannot typically be implemented in more compact andmobile devices, and typically cannot be performed in a real-time manner.Therefore, there are substantial drawbacks with current image processingapproaches for image magnification.

SUMMARY

According to an one or more embodiments, there is provided an imageprocessing method, including performing trajectory estimation on pixelsor blocks of a current frame, including pixels or blocks correspondingto a feature region of the current frame, to identify trajectoryestimated pixels when trajectory information of a previous frame isavailable, classifying the trajectory estimated pixels or blocks into afeature region and individually classifying pixels or blocks of thecurrent frame, excluding the trajectory estimated pixels or blocks,respectively into one of the feature region and a uniform region inaccordance with each respective spatial spectrum, matching featureregion pixels or blocks of the feature region with feature region pixelsor blocks of at least one previous frame, and projecting matched featureregion pixels or blocks of the at least one previous frame and thecorresponding trajectory estimated pixels or blocks into a highdefinition frame having a higher definition than the current frame, andinterpolating pixels of the high definition frame, excluding theprojected matched feature region pixels or blocks and the correspondingtrajectory estimated pixels or blocks, based on the matched featureregion pixels or blocks and the corresponding trajectory estimatedpixels or blocks, and based on pixels or blocks of the uniform region.

According to an one or more embodiments, there is provided an imageprocessing method of producing a high definition video from a lowdefinition video, the method including individually classifying pixelsor blocks of a current frame of the low definition video into a featureregion and a uniform region in accordance with a respective spatialspectrum of each pixel or block, with pixels or blocks classified intothe feature region respectively being feature region pixels or blocks,and producing a high definition video corresponding to the featureregion based on a plurality of frames of the low definition video withrespect to the feature region and based on pixel value information fromat least one previous frame to the current frame, and producing a highdefinition video corresponding to the uniform region using interpolationat least between pixels or blocks of the current frame classified intothe uniform region.

According to an one or more embodiments, there is provided an imageprocessing method producing a high definition video from a lowdefinition video, the method including determining feature region pixelsof an i-th frame of the low definition video by performing respectivetrajectory estimation on feature region pixels of an (i−1)-th frame ofthe low definition video, determining pixel values of pixels of a frameof the high definition video corresponding to the i-th frame by matchingthe feature region pixels of the i-th frame with feature region pixelsof at least one previous frame corresponding to the feature regionpixels of the i-th frame, and determining, using interpolation, pixelvalues of the frame of the high definition video corresponding to thei-th frame that are not determined by the matching, wherein the matchingof the feature region pixels of the i-th frame with feature regionpixels of the at least one previous frame includes matching the featureregion pixels of the i-th frame with only feature region pixels of theat least one previous frame, with the at least one previous frameincluding pixels in addition to the feature region pixels.

According to an one or more embodiments, there is provided an imageprocessing method, including receiving an image and trajectoryinformation of the received image, individually classifying pixels orblocks of the received image into a feature region and a uniform regionin accordance with a respective spatial spectrum of each pixel of block,with pixels or blocks classified into the feature region respectivelybeing feature region pixels or blocks, and producing a high definitionimage by interpolating at least between pixels or blocks within theuniform region and interpolating at least between pixels or blockscorresponding to the feature region pixels based on the receivedtrajectory information.

According to an one or more embodiments, there is provided an imageprocessing method, including individually classifying pixels or blocksof a first frame respectively into one of the feature region and auniform region of the first frame in accordance with each respectivespatial spectrum and storing pixel values and locations of pixels orblocks classified into the feature region of the first frame in aregistration memory, as feature region pixels or blocks of the firstframe, performing trajectory estimation on pixels or blocks of a secondframe, subsequent to the first frame, including obtaining pixel valuesand locations of the feature region pixels or blocks of the first framefrom the registration memory, and identifying trajectory estimatedpixels or blocks of the second frame based on the obtained featureregion pixels or blocks of the first frame, and storing pixel values andlocations of the trajectory estimated pixels of blocks of the secondframe in the registration memory, classifying the trajectory estimatedpixels or blocks of the second frame into a feature region andindividually classifying pixels or blocks of the second frame, excludingthe trajectory estimated pixels or blocks of the second frame,respectively into one of the feature region and a uniform region of thesecond frame in accordance with each respective spatial spectrum,matching feature region pixels or blocks of the feature region of thesecond frame with feature region pixels or blocks of the first frame,and projecting matched feature region pixels or blocks of the firstframe and the corresponding trajectory estimated pixels or blocks of thesecond frame into a high definition frame having a higher definitionthan the second frame, and interpolating pixels of the high definitionframe, excluding the projected matched feature region pixels or blocksof the first frame and the corresponding trajectory estimated pixels orblocks of the second frame, based on the matched feature region pixelsor blocks of the first frame and the corresponding trajectory estimatedpixels or blocks of the second frame, and interpolating pixels of thehigh definition frame based on pixels or blocks of the uniform region ofthe second region.

According to an one or more embodiments, there is provided an imageprocessing system, including a trajectory estimation unit to performtrajectory estimation on pixels or blocks of a current frame, includingpixels or blocks corresponding to a feature region of the current frame,and classifying trajectory estimated pixels of blocks into the featureregion, a region classification unit to individually classify pixels orblocks of the current frame, excluding the trajectory estimated pixelsor blocks of the current frame, respectively into one of the featureregion and a uniform region in accordance with each respective spatialspectrum, a registration unit to match feature region pixels or blocksof the feature region of the current frame with feature region pixels orblocks of at least one previous frame, and to project matched featureregion pixels or blocks of the at least one previous frame and thecorresponding trajectory estimated pixels or blocks into a highdefinition frame having a higher definition than the current frame, anda high resolution interpolation unit to interpolate pixels or blocks ofthe high definition frame, excluding the projected matched featureregion pixels or blocks and the corresponding trajectory estimatedpixels or blocks, based on the matched feature region pixels or blocksand the corresponding trajectory estimated pixels or blocks, and basedon pixels or blocks of the uniform region.

According to an one or more embodiments, there is provided an imageprocessing system producing a high definition video from a lowdefinition video, the system including a region classification unit toindividually classify pixels or blocks of a current frame of the lowdefinition video into a feature region and a uniform region inaccordance with a respective spatial spectrum of each pixel or block,with pixels or blocks classified into the feature region respectivelybeing feature region pixels or blocks, and a high resolutioninterpolation unit to produce a high definition video corresponding tothe feature region based on a plurality of frames of the low definitionvideo with respect to the feature region and based on pixel valueinformation from at least one previous frame, and to produce a highdefinition video corresponding to the uniform region using interpolationat least between pixels or blocks of the current frame classified intothe uniform region.

According to an one or more embodiments, there is provided an imageprocessing system producing a high definition video from a lowdefinition video, the system including a trajectory estimation unit todetermine feature region pixels of an i-th frame of the low definitionvideo by performing respective trajectory estimation on feature regionpixels of an (i−1)-th frame of the low definition video, a registrationunit to determine pixel values of pixels of a frame of the highdefinition video corresponding to the i-th frame by matching the featureregion pixels of the i-th frame with feature region pixels of at leastone previous frame corresponding to the feature region pixels of thei-th frame and storing matched feature region pixels in a registrationmemory, and an interpolation unit to determine, using interpolation,pixel values of the frame of the high definition video corresponding tothe i-th frame that are not determined by the matching, wherein thematching of the feature region pixels of the i-th frame with featureregion pixels of the at least one previous frame includes matching thefeature region pixels of the i-th frame with only feature region pixelsof each of the at least one previous frame, with the at least oneprevious frame including pixels in addition to the feature regionpixels.

Additional aspects and/or advantages will be set forth in part in thedescription which follows and, in part, will be apparent from thedescription, or may be learned by practice of one or more embodiments ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects will become apparent and more readilyappreciated from the following description of one or more embodiments,taken in conjunction with the accompanying drawings of which:

FIG. 1 is a flowchart illustrating a method of producing high definitionvideo from low definition video according to one or more embodiments;

FIG. 2 illustrates an adjustment of a classification operation tomaintain a minimal distance between feature region pixels, according toone or more embodiments;

FIG. 3 illustrates an adjustment of a classification operation to alsomaintain a minimal distance between feature region pixels, according toone or more embodiments;

FIG. 4 illustrates a division of a current frame into N×M-numberedblocks to determine feature region pixels, according to one or moreembodiments;

FIG. 5 illustrates classifying portions of a current frame into one of afeature region and a uniform region, according to one or moreembodiments;

FIGS. 6A-6B illustrate trajectory estimation being performed on featureregion pixels, according to one or more embodiments;

FIG. 7 illustrates trajectory estimation being performed on featureregion pixels at a sub-pixel unit, according to one or more embodiments;

FIG. 8 illustrates feature region pixels of frames of a low definitionvideo, and pixel values and locations of adjacent or neighboring pixels,to be stored, according to one or more embodiments;

FIG. 9 illustrates matching feature region pixels, according to one ormore embodiments;

FIG. 10 illustrates a method of preventing errors from occurring in amatching of feature region pixels, according to one or more embodiments;

FIG. 11 illustrates a frame of a high definition video corresponding toa frame of a low definition video, according to one or more embodiments;

FIG. 12 illustrates performance of an interpolating of pixels of a frameof a high definition video, according to one or more embodiments;

FIGS. 13A-13C illustrate image processing systems producing a highdefinition image from a low definition image, according to one or moreembodiments; and

FIG. 14 illustrates a conventional image processing system for producinghigh definition video.

DETAILED DESCRIPTION

Reference will now be made in detail to one or more embodiments,illustrated in the accompanying drawings, wherein like referencenumerals refer to like elements throughout. In this regard, embodimentsof the present invention may be embodied in many different forms andshould not be construed as being limited to embodiments set forthherein. Accordingly, embodiments are merely described below, byreferring to the figures, to explain aspects of the present invention.

The term ‘video’ used throughout the present specification may designateall types of plural screens or images, such as screens or images thatmay be reproducible on a display. An ‘image’ may be a single frame of avideo, e.g., with plural contiguous frames together making up the video,though all frames are not necessarily needed to be contiguous for thecollection of images to be understood to be a video.

FIG. 1 is a flowchart illustrating a method of producing high definitionvideo from low definition video, according to one or more embodiments.

Referring to FIG. 1, in operation S110, a current frame or image of alow definition video may be classified into one of a feature region anda uniform region in accordance with a spatial spectrum, for example. The‘feature region’ may designate portions of the frame where changes arerelatively significant in image information. For example, when it isdifficult to extract a pixel value of a pixel within a correspondingportion of the frame merely from a pixel value of an adjacent pixel,e.g., due the image information within the portion of the frame havingdifferent intensities and/or colors, such as when the portion of theframe has a checkered appearance, that portion of the frame may beclassified into the feature region. The ‘uniform region’ may designateportions of the frame where changes may be relatively insignificant inimage information. For example, when it is easy to extract the pixelvalue of the pixel within a corresponding portion of the frame merelyfrom a pixel value of an adjacent pixel, e.g., due to image informationof that portion of the frame generally having the same intensitiesand/or colors, this portion of the frame may be classified into theuniform region. The classifying of portions of the current frame intothe feature region and the uniform region will be described in greaterdetail below. Additionally, as also discussed in greater detail below,pixels within a frame may be selectively classified into one of thefeature region and uniform region, such that some of the pixels of thecurrent frame are considered to be within the feature region and some ofthe pixels of the current frame are considered to be within the uniformregion. In one or more embodiments, the pixels classified into thefeature region would not be the same pixels classified into the uniformregion, and not all pixels of the low definition frame are required tobe classified into the feature region or uniform region.

In one or more embodiments, if trajectory information of a previousframe is available, trajectory estimation may be performed within orbefore operation S110. If trajectory estimation of a pixel of the lowdefinition frame is performed within or before the operation S110, andit is accordingly determined that the pixel is traced by another pixelof the previous frame, any resulting trajectory estimated pixels of thelow definition frame may be automatically classified as feature regionpixels of the low definition frame. For example, if a first lowdefinition frame of the low definition video is being reviewed, suchtrajectory information of a previous frame may not be available.However, when the next low definition frame is analyzed the trajectoryinformation for trajectory estimating pixels of the next low definitionframe may be available based upon the classification of pixels of thecurrent low definition frame. The remaining pixels of the low definitionframe may still be classified into the feature region or uniform region.Similarly, the classification of operation S110 may be only performed onselect pixels of the low definition frame, as some pixels surrounding analready classified feature region pixel may selectively not beclassified, e.g., based on a potential maintenance of a minimal distancebetween classified pixels of the feature region. Pixel values for atleast some of these surrounding pixels may still be stored, as beingadjacent or neighboring pixels of the pixel classified into the featureregion. In one or more embodiments, there may be no maintained minimaldistance between uniform region classified pixels.

Accordingly, in operation S120, a feature region based high definitionframe may be produced based on the feature region. According to one ormore embodiments, a corresponding high definition video may then beproduced based on a plurality of frames of the low definition video withrespect to the feature region.

In operation S130, a uniform region based high definition frame may beproduced based on the uniform region. According to one or moreembodiments, a corresponding high definition video may be produced usinginterpolation with respect to pixels of the current frame that areclassified as being within the uniform region. That is, thecorresponding high definition video may be generated based on eachsingle frame of the low definition video, by itself, with respect topixels within the uniform region of each frame. Operations S120 and S130may be performed in parallel.

In operation S140, a high definition video may be produced based on theabove described processes S110-130. According to one or moreembodiments, high definition videos may be separately produced withrespect to each of the classified feature region and the uniform regionof each low definition frame, e.g., analyzed to produce respective highdefinition frames that are combined to generate a feature region basedhigh definition video and a uniform region based high definition video,and then the high definition video based on the feature region and thehigh definition video based on the uniform region may be combined toproduce a single high definition video representing both feature anduniform regions.

The classifying of a current frame into the feature region and theuniform region according to one or more embodiments will now bedescribed in greater detail. Any of the below classification operations,to classify a pixel or block of pixels into a feature region or uniformregion, may be performed alone or in combination for classification ofpixels or blocks of pixels into at least the feature region and uniformregion.

Accordingly, in one or more embodiments, pixels or blocks of a currentframe having a relatively high spatial spectrum may be classified intothe feature region, and pixels or blocks of the current frame having arelatively low spatial spectrum may be classified into the uniformregion.

According to one or more embodiments, respective pixels of the currentframe of the low definition frame may be classified into the featureregion and/or the uniform region in accordance with respective gradientvalues of pixels of the current frame. When a gradient value of a pixelmeets or is equal to or greater than a reference value, or one or morereference values, the pixel of the current frame may be classified intothe feature region, and when the gradient value does not meet or is lessthan the reference value(s), the pixel of the current frame may beclassified into the uniform region. The ‘gradient value’ may be a rateof change of information about an intensity of the pixel, a color of thepixel, and the like, for example. According to one or more embodiments,the gradient value including a rate of change in an x-axis and a rate ofchange in a y-axis may be used. For example, the gradient value may berepresented as |G|=|G_(x)|+|G_(y)|, where |G| denotes a gradient value,|G_(x)| denotes a rate of change in an x-axis, and |G_(y)| denotes arate of change in a y-axis. According to one or more embodiments, thegradient value may be determined based on either the rate of change inthe x-axis or the rate of change in the y-axis, or both. Though theseembodiments describe the gradient with regard to an arbitrary pixel, thegradient may be with regard to a pixel and/or one or more blocks ofpixels, e.g., a gradient for a block based upon a calculated gradientfor one or more pixels of the block or the block as a whole, such asthrough an averaging process of the pixels within the block. Forexample, an intensity of a block of pixels or color of the block ofpixels may be based upon an averaging of pixel values within the block,though alternative representations of the block or pixels within blocksmay be generated by alternative techniques. To avoid repetition hereinfor the below described embodiments, the above meaning of a gradient andhow a gradient may be calculated are equally intended to be applied toembodiments below that also use the ‘gradient’ term.

According to one or more embodiments, respective pixels of the currentframe of the low definition video may be classified into the featureregion and/or the uniform region in accordance with respective seconddifferential values of pixels of the current frame. In an embodiment,when a second differential value of a pixel of the current frame meetsor is equal to or greater than a reference value, or one or morereference values, the pixel of the current frame may be classified intothe feature region, and when the second differential value does not meetor is less than the reference value(s), the pixel of the current framemay be classified into the uniform region. According to one or moreembodiments, the ‘second differential value’ may be calculated accordingto a Laplacian technique, Laplacian of Gaussian (LOG) technique, and thelike, for example. Similar to above, though these embodiments describethe second differential with regard to an arbitrary pixel, the seconddifferential may be with regard to a pixel and/or one or more blocks ofpixels. To avoid repetition herein for the below described embodiments,the above meaning of a second differential and how a second differentialmay be calculated are equally intended to be applied to embodimentsbelow that also use the ‘second differential’ term.

According to one or more embodiments, respective pixels of the currentframe of the low definition video may be classified into the featureregion and/or the uniform region in accordance with a dispersion of anintensity difference between pixels of the current frame and respectiveadjacent or neighboring pixels.

Herein, the term ‘neighboring pixel’ may represent one or more pixelsthat are adjacent, meaning directly adjacent, to a current pixel andpixels that are neighboring the current pixel, e.g., a pixel that is adefined degree of separation from the current pixel. Neighboring pixelsare pixels near an arbitrary pixel and separated a defined degree fromthe arbitrary pixel, with this degree being potentially adjustable, inone or more embodiments. For example, in an embodiment, when the degreeis 3 the neighboring pixels may include pixels adjacent to the currentpixel and pixels up to pixels 3 degrees away from the current pixel,i.e., pixels separated from the arbitrary pixel by three pixels or less.The degree of separation also is not required to be equal in alldirections surrounding the arbitrary pixel. Below, though referenceswill be made to one or more adjacent pixels of a pixel, unless indicatedotherwise it should be understood that such embodiments are equallyavailable for greater degrees of separation from one or more neighboringpixels, i.e., each embodiment referencing adjacent pixels is alsointended to be available neighboring pixels in addition, or as analternative, to the adjacent pixels, according to the respectiveconsideration of the one or more adjacent pixels in each embodiment.

Accordingly, when the dispersion of the intensity difference between apixel of the current frame and an adjacent pixel meets or is equal to orgreater than a reference value, or one or more reference values, thepixel of the current frame may be classified into the feature region,and when the dispersion of the intensity difference does not meet or isless than the reference value(s), the pixel of the current frame may beclassified into the uniform region. According to one or moreembodiments, the ‘dispersion of the intensity difference’ may designatea difference between the intensity difference of the pixel of thecurrent frame and the adjacent pixel with an average value of theintensity difference, and may be a measure of a dispersion degree of theintensity difference based on the average value. Similar to above,though these embodiments describe the dispersion with regard to anarbitrary pixel and another adjacent or neighboring pixel, thedispersion may be with regard to a pixel and/or one or more blocks ofpixels compared to another adjacent or neighboring pixel and/or blocksor pixels. To avoid repetition herein for the below describedembodiments, the above meaning of a dispersion and how a dispersion maybe calculated are equally intended to be applied to embodiments belowthat also use the ‘dispersion’ term.

According to one or more embodiments, pixels or blocks of the currentframe of the low definition video may be classified into the featureregion and/or the uniform region in accordance with a difference betweenpixel values of pixels of the current frame and one or more of theirrespective adjacent pixels. When this difference between the pixelvalues meets or is equal to or greater than a reference value, or one ormore reference values, the corresponding pixel of the current frame maybe classified into the feature region, and when the difference betweenthe pixel values does not meet or is less than the reference value, thecorresponding pixel of the current frame may be classified into theuniform region. In an embodiment, if multiple adjacent pixels areconsidered, the difference could be determined with one or more of theadjacent pixels individually or based upon an averaging of informationfrom the adjacent pixels, as only an example. According to one or moreembodiments, the ‘pixel value’ may be information about a color valueand an intensity value of the pixel, and the like, for example. Similarto above, though these embodiments describe the difference between pixelvalues with regard to an arbitrary pixel and at least one other adjacentor neighboring pixel, the difference between pixel values may be withregard to a pixel and/or one or more blocks of pixels compared toanother adjacent or neighboring pixel and/or blocks or pixels. To avoidrepetition herein, the below described embodiments regarding the meaningof a difference between pixel values, how a difference between pixelvalue may be calculated, and the term ‘pixel value’ are equally appliedto embodiments below that also use the ‘difference between pixel value’term and the ‘pixel value’ term.

Additionally, in one or more embodiments, based upon a classification ofone pixel of a current frame into the feature region there may not be aneed to classify the pixels near the classified feature region pixelinto either of into the feature region and the uniform region inaccordance with the spatial spectrum, i.e., it may not be necessary toperform the classification process on all pixels of the current frame,as the classification process can be bypassed for pixels near thealready classified feature region pixels and these bypassed pixels mayeither be not classified into either of the feature region or uniformregion or merely classified into the feature region as only being pixelsnear the feature region pixel. Regardless, in one or more embodiments,pixel information for at least some of these near pixels may still bestored, e.g., as pixel information of adjacent or neighboring pixels ofthe classified feature region pixel. This selective classificationapproach may be performed so that a minimal distance between pixelsclassified into the feature region (hereinafter, referred to as ‘featureregion pixels’) is maintained. The minimal distance may be equated to ordependent on the above described degree of separation of neighboringpixels, or the minimal distance may directly correspond to the degree ofseparation of neighboring pixels.

Thus, according to one or more embodiments, an arbitrary pixel may beclassified into either of the feature region and/or the uniform regionin accordance with the spatial spectrum. Next, when the arbitrary pixelhas been classified into the feature region, classification into eitherof the feature region or uniform region may only need to be subsequentlydone on a pixel that is separated from the arbitrary pixel by a certainminimal distance, thereby maintaining the minimal distance between thefeature region pixels. Rather, when the arbitrary pixel is classified asbeing included in the uniform region (hereinafter, referred to as‘uniform region pixel’), pixels adjacent to or near the arbitrary pixelmay still be classified into either of the feature region and theuniform region, regardless of such a minimal distance. By adjusting theminimal distance, the number of the feature region pixels of the currentframe may be adjusted.

According to one or more embodiments, the method may further includereceiving an input of the minimal distance, and/or receiving an input ofthe degree of separation controlling whether pixels are consideredneighboring. As only an example, this minimal distance may be based on adistance between feature region pixels designed to prevent overlap ofrespective trajectories i.e., if the feature region pixels are too closeto each other in space, a trajectory estimation from some of thesefeature region pixels may overlap.

Hereinafter, an adjusting of the classification operation to maintainthe minimal distance between the feature region pixels will be furtherdescribed with reference to FIGS. 2 and 3.

FIG. 2 illustrates an adjustment of the classification operation tomaintain a minimal distance between feature region pixels, according toone or more embodiments.

Referring to FIG. 2, in one or more embodiments, an arbitrary pixel 220of a current frame 210 may be classified into either of a feature regionand a uniform region in accordance with a respective spatial spectrum.When the pixel 220 is classified into the uniform region, furtherclassification of pixels adjacent to the pixel 220 into the featureregion or uniform region may next be performed to classify each of theadjacent pixels into either of the feature region and the uniform regionin accordance with their respective spatial spectrum, i.e., regardlessof any minimal distance between uniform region pixels. When the pixel220 is classified into the feature region, classification into thefeature region or uniform region of pixels within a radius of a minimaldistance 250 from the pixel 220 may selectively not be performed.Rather, the classification into the feature region or the uniform regionbeing performed for a pixel 230 outside the minimal distance 250. In oneor more embodiments, when the pixel 220 is classified to be a featureregion pixel, the pixels within the minimal distance 250 may not beclassified, though information about the pixels are stored, e.g., aspixels adjacent, neighboring, or near the feature region pixel 220, forsubsequent interpolation purposes, for example. As an alternative, thepixels within the minimal distance 250 may automatically be classifiedinto the feature region, though differently from the pixel 220. Herein,in one or more embodiment, when such a minimal distance is applied, theterm feature region pixel will refer to the pixel from which the minimaldistance originates.

Here, again, if the pixel 230 is classified as being one of the featureregion pixels, classification of pixel 240 outside a minimal distanceradius from the pixel 230 may be performed, while classification may notbe performed for pixels within the minimal distance from pixel 230.Accordingly, the classification of pixels may be adjusted or controlledto maintain a minimal distance among the feature region pixels 220, 230,and 240 (e.g., when pixel 240 is classified into the feature region).

FIG. 3 illustrates an adjustment of a classification operation tomaintain a minimal distance between feature region pixels, according toone or more embodiments.

Referring to FIG. 3, an arbitrary pixel 320 of a current frame 310 of alow definition video may be classified into either of the feature regionand the uniform region in accordance with a respective spatial spectrum.When the pixel 320 is classified into the uniform region, classificationof pixels adjacent to the pixel 320 may still be performed, regardlessof any minimal distance between the uniform region pixels. When thepixel 320 is classified into the feature region, classification may notbe performed for pixels separated from the pixel 320 in an x-axis by aminimal distance 370 or less, while classification may be performed forpixel 330 spaced apart from the pixel 320 by at least the minimaldistance 370 in the x-axis.

Next, regardless of whether the pixel 330 is classified to be a uniformregion pixel or feature region pixel, classification of pixel 340separated from the pixel 330 in an x-axis by at least the minimaldistance may be performed.

According to one or more embodiments of FIG. 3, the minimal distancesrelied upon to determine whether to perform classification may beadjusted so a classification is performed based only on maintenance of aminimal distance in the x-axis, and a minimal distance in the y-axis maynot be controlling on whether to perform classification. Accordingly,when both pixel 320 and pixel 340 are classified to be one of thefeature region pixels, since pixel 350 is separated from the pixel 340by at least a minimal distance in the x-axis, different from the y-axisdistance, classification of the pixel 340 may be performed. Similarly,when the pixel 340 is classified to be one of the feature region pixels,if pixel 360 is separated from the pixel 340 by at least the minimaldistance in the x-axis, different from the y-axis distance,classification of the pixel 360 into either of the feature region andthe uniform region may be performed.

According to one or more embodiments, the minimal distances relied uponto determine whether to perform classification may alternatively beadjusted so a classification is performed based only on maintenance ofonly one minimal distance in the x-axis or minimal distance in they-axis, or the minimal distances relied upon to determine whether toperform classification may be based on both at least one minimaldistance in the x-axis and at least one minimal distance in the y-axisdifferent from the minimal distance in the y-axis. Similarly, minimaldistances may be different for positive or negative directions in the xor y-axes distances, and may be differently adjusted based on the regionof the current frame. As only an example, regions of interest may bepredetermined so minimal distances may be adjusted to provide greaterpreference to one area over another.

According to one or more embodiments, pixels, being separated fromclassified feature region pixels by at least such minimal distances, andalso respectively having a relatively high spatial spectrum mayaccordingly also be classified into the feature region. Accordingly, inone or more embodiments, pixels within a minimal distance from aclassified feature region pixel, even though they may respectively havea relatively low spatial spectrum, e.g., typically available forclassification into the uniform region, may not be classified intoeither of the feature region or the uniform region. In one or moreembodiments, even though the pixels within the minimal distance may beclassifiable into the uniform region, the pixels within the minimaldistance are automatically classified into the feature region. A pixelbeyond a minimal distance from the feature region pixel and having arelatively low spatial spectrum may still be classified into the uniformregion. Hereinafter, pixels being separated from a respective closestclassified feature region pixel by at least minimal distances will bedefined as a ‘separated pixel’ or ‘separated pixels’.

According to one or more embodiments, separated pixels may berespectively classified into the feature region and the uniform regionin accordance with their respective gradient values, as discussed above.In this instance, when the respective gradient values of the separatedpixels meet or are equal to or greater than a reference value, or one ormore reference values, the separated pixels may be respectivelyclassified into the feature region, and when the respective gradientvalues of the separated pixels do not meet or are less than thereference value(s), the separated pixels may be classified into theuniform region.

According to one or more embodiments, the separated pixels may berespectively classified into either of the feature region and theuniform region in accordance with second differential values, asdiscussed above, of the separated pixels. When the second differentialvalues of the separated pixels meets or are equal to or greater than areference value, or one or more reference values, the separated pixelsmay be classified into the feature region, and when the seconddifferential values of the separated pixels do not meet or are less thanthe reference value(s), the separated pixels may be classified into theuniform region.

According to one or more embodiments, the separated pixels may berespectively classified into either of the feature region and theuniform region in accordance with a dispersion of an intensitydifference, as discussed above, between the separated pixels andadjacent pixels

When a dispersion of an intensity difference, as discussed above,between the separated pixels and the respective adjacent pixels meets oris equal to or greater than a reference value, or one or more referencevalues, the separated pixels may respectively be classified into thefeature region, and when the dispersion of the intensity differencebetween the separated pixels and the respective adjacent pixels does notmeet or is less than the reference value(s), the separated pixels may berespectively classified into the uniform region.

According to one or more embodiments, the separated pixels may berespectively classified into either of the feature region and theuniform region in accordance with a difference of pixel values, asdiscussed above, between the separated pixels and the respectiveadjacent pixels. In this instance, when the difference of pixel valuesmeets or is equal to or greater than a reference value, or one or morereference values, the separated pixels may be respectively classifiedinto the feature region, and when the difference of pixel values doesnot meet or is less than the reference value(s), the separated pixelsmay be respectively classified into the uniform region.

In one or more embodiments, the current frame may be divided intoN×M-numbered blocks, with at least one of N and M being greater than 1.In one or more embodiments, any one pixel from any one block theN×M-numbered blocks may be classified as including a feature regionpixel, and based upon comparisons between other classified featureregion pixels of the block or based merely based upon the feature regionpixel being the first classified feature region pixel of the block, forexample, the entire block may be classified into the feature region withat least one of the pixels of the block being classified as being afeature region pixel. For example, one pixel of a feature region blockmay be classified as the feature region pixel and the remaining pixelsof the block may be classified as neighboring pixels of the featureregion pixel of the block. Similarly, one pixel of a uniform regionblock may be classified as the uniform region pixel and the remainingpixels of the block may be classified as neighboring pixels of theuniform region pixel of the block, e.g., such that the uniform regionpixel may be used to represent one or more of the pixels of the blockduring interpolation.

FIG. 4 illustrates a division of a current frame 410 into suchN×M-numbered blocks to determine feature region pixels, according to oneor more embodiments. In one or more embodiments below, if one pixel of ablock of the N×M-numbered blocks is classified as a feature region pixelfor the block, then the remaining pixels within the block may also beclassified into the feature region and potentially all pixels within theblock may be identified as adjacent or neighboring pixels of the featureregion pixel of the block. Similarly, if no pixel within the block meetsone or more, or all, of the aforementioned classification operationrequirements, then the entire block may be classification into theuniform region.

Referring to FIG. 4, a method of producing high definition video fromlow definition video according to one or more embodiments may includedividing the current frame 410 into M blocks along an x-axis, and into Nblocks along a y-axis. With respect to all pixels within a block 420,from among the N×M-numbered blocks, a gradient value may be estimated.When a pixel having the greatest gradient value within the block 420 hasa gradient value that meets or is equal to or greater than a referencevalue, or one or more reference values, the pixel may be classified tobe a feature region pixel. When the pixel having the greatest gradientvalue within the block 420 has a gradient value that does not meet or isless than the reference value(s), the pixel may be classified to be auniform region pixel. Here, in one or more embodiments, additionalpixels of the block may be classified as being feature region pixelsbased upon their gradient value and/or proximity within the blockrelative to the pixel having the greatest gradient value. By adjusting amagnitude of either N or M, the potential number of feature regionpixels classified in the current frame may be controlled.

According to one or more embodiments, a reference value may be input andreceived for classifying a pixel, having the greatest gradient valuewithin a corresponding block, into either of the feature region pixeland the uniform region pixel.

In one or more embodiments, a pixel having the greatest spatial spectrumwithin a block, e.g., of the N×M-numbered blocks, and a spatial spectrummeeting or being equal to or greater than a reference value, or one ormore reference values, may be classified as being one of the featureregion pixels. When the pixel having the greatest spatial spectrumwithin the block has a spatial spectrum that does not meet or is lessthan the reference value(s), all pixels within the block may beclassified into the uniform region. Here, in one or more embodiments,additional pixels of the block may be classified as being feature regionpixels based upon their respective spatial spectrum and/or proximitywithin the block relative to the pixel having the greatest spatialspectrum.

According to one or more embodiments, a pixel, having the greatestsecond differential value, as discussed above, within a block, e.g., ofthe N×M-numbered blocks, and having a second differential value meetingor being equal or greater than a reference value, or one or morereference values, may be classified to be one of the feature regionpixels. When the pixel having the greatest second differential valuewithin the block has a second differential value that does not meet oris less than the reference value(s), all pixels within the block may beclassified into the uniform region. Here, in one or more embodiments,additional pixels of the block may be classified as being feature regionpixels based upon their respective second differential value and/orproximity within the block relative to the pixel having the greatestsecond differential value

According to one or more embodiments, a pixel having the greatestdispersion of an intensity difference, as discussed above, with arespective adjacent pixel within a block, e.g., of the N×M-numberedblocks, and having a dispersion of the intensity difference meeting orequal to or greater than a reference value, or one or more referencevalues, may be classified as being one of the feature region pixels.When the pixel having the greatest dispersion of the intensitydifference with a respective adjacent pixel within the block has adispersion of the intensity difference that does not meet or is lessthan the reference value(s), all pixels within the block may beclassified into the uniform region. Here, in one or more embodiments,additional pixels of the block may be classified as being feature regionpixels based upon their dispersion and/or proximity within the blockrelative to the pixel having the dispersion.

According to one or more embodiments, a pixel having the greatestdifference between pixel values with a respective adjacent pixel withina block, e.g., of the N×M-numbered blocks, and having a difference ofthe pixel values meeting or equal to or greater than a reference value,or one or more reference values, may be classified to be one of thefeature region pixels. When the pixel having the greatest differencebetween the pixel values with a respective adjacent pixel within theblock has a difference between the pixel values that does not meet or isless than the reference value(s), all pixels within the block may beclassified into the uniform region. Here, in one or more embodiments,additional pixels of the block may be classified as being feature regionpixels based upon their respective difference between pixel valuesand/or proximity within the block relative to the pixel having thegreatest difference between pixel values.

FIG. 5 illustrates classifying portions of a current frame into one of afeature region and a uniform region, according to one or moreembodiments. In one or more embodiments, the portions of a frame may bepixels or blocks of pixels, and all pixels of blocks of pixels of theframe may be classified into the feature region and uniform region.Alternatively, in one or more embodiments, some pixels within the blocksmay not be classified if a minimal distance is not met, as only anexample.

Referring to FIG. 5, the pixel 511 is a previously classified featureregion pixel in a previous frame 510, and the pixel 521 and the pixel522 may be to-be-classified pixels of a current frame 520. Based uponstored information, e.g., in a registration memory, the pixel 521 may bedetermined to be corresponding to pixel 511 based on trajectoryestimation 530 of pixel 511. As pixel 511 of the previous frame 510 wasa feature region pixel, the pixel 521 may similarly be classified as afeature region pixel for the current frame 520, i.e., without requiringclassification. The pixel 521 may be considered to have been traced bythe trajectory estimation 530. However, without such an indication of atrajectory tracing of a feature region pixel in the previous frame 510,classification may still need to be performed for the pixel 522, withthe pixel 522 being classified into one of the feature region and theuniform region in accordance with the spatial spectrum of pixel 522.Information of each pixel that is classified as a feature region pixelmay be stored in the aforementioned registration memory, withinformation of the adjacent or neighboring pixels and pixels within aminimal spacing of the classified feature region pixel.

According to one or more embodiments, trajectory estimation may beperformed only from feature region pixels of the previous frame topixels of the feature region of a current frame. Trajectory estimationmay be performed prior to classification of pixels of the current frameinto the feature region and the uniform region, in which case a pixel ofthe current frame that is trajectory estimated based upon a featureregion pixel of a previous frame may be automatically classified intothe feature region, and further classified as a feature region pixels ofthe feature region. Thus, classification of the trajectory estimatedpixel would not be performed, as the trajectory estimated pixel wouldhave already been classified into the feature region.

That is, there may be no trajectory estimation from a pixel classifiedinto the uniform region of the previous frame to a pixel of the currentframe. Rather, a high definition video corresponding to the uniformregion of the current frame may be separately produced based upon pixelsof only the uniform region using interpolation adopting informationabout only the current frame, for example. Accordingly, in one or moreembodiments, interpolation of the uniform region and/or uniform regionpixels of the current frame may be performed using a single frame, e.g.,the current frame. The ‘trajectory estimation’ may be reserved forpixels of the current frame and feature region pixels of a previousframe. Accordingly, it may only be necessary to store pixel values andlocations of the feature region pixels, and respective adjacent orneighboring pixels, for previous frames. If the feature region pixel ofthe previous frame is also a trajectory estimated feature region pixel,i.e., it was estimated from a feature region pixel of a further previousframe, then trajectory information may also be stored, identifying thefeature region pixel of the further previous frame. Accordingly, byperforming trajectory estimation from this feature region pixel in theprevious frame to a pixel in the current frame, the pixel of the currentframe may be trajectory estimated and the pixel information for at leasttwo previous feature region pixels corresponding to the trajectoryestimated pixel may be known. For example, FIG. 9 illustrates threeprevious frames of the same lineage.

The trajectory estimation may include designating a select pixel of oneframe (e.g., a current frame) having a highest correlation with afeature region pixel of another frame (e.g., previous frame). Thiscorrelation between respective pixels of a previous frame and pixels ofa current frame may be based on information including an intensityvalue, a gradient value, a second differential value, a spatial spectrumvalue, and a pixel value of a pixel, and the like, for example basedupon the above discussions of the same terms. For example, according toone or more embodiments, a sum of absolute difference (SAM) method ofperforming the trajectory estimation using an intensity difference maybe used, for example. In one or more embodiments, the trajectoryestimation may identify the most probable pixel coordinates of thecurrent frame for a feature region pixel of a previous frame based onthe trajectory estimation, and then correlation may be performed tofurther identify the most appropriate pixel from the probably pixelcoordinates, e.g., candidate coordinates, corresponding of the currentframe for the feature region pixel of the previous frame.

In a case where there is a detected transition, occlusion, and the like,any results of a trajectory estimation with respect to a pixel of thecurrent frame corresponding to a feature region pixel of the previousframe may not be stored in the registration memory, or at leastinterpolation of the corresponding high definition frame correspondingto the current frame would not be based upon this trajectory estimation.Due to the transition, occlusion, and the like, if the correspondingtrajectory estimation results were used within the interpolation ofpixels of the high definition frame there may be a greater probabilityof an erroneous interpolation. In an embodiment, the trajectoryestimation may fail to be performed with respect to the pixel of thecurrent frame corresponding to the feature region pixel of the previousframe when there is a detected transition, occlusion, and the like.Accordingly, here, as the trajectory estimation has not been performed,the registration memory would also not be updated with the informationof the correspondence between the feature region pixel of the previousframe and the current pixel. When the trajectory estimation results arenot stored or the trajectory estimation fails to be performed for aparticular pixel of the current frame, pixels excluding the particularpixel of the current frame, on which the trajectory estimation havingbeen performed, corresponding to the feature region pixel of theprevious frame, may be classified into the feature region and theuniform region in accordance with their respective spatial spectrum. Ifthe current frame has been divided into the N×M-numbered blocks, thenanother pixel, of the same block as the particular pixel for whichtrajectory estimation has been decided not appropriate, e.g., because ofthe transition, occlusion, or the like, may be classified into thefeature region based upon the spatial spectrum and/or estimatedtrajectory from the feature region pixel of the previous frame. As notedabove, in one or more embodiments, this other feature region pixel maythen be considered the feature region pixel for all pixels of the block.

FIGS. 6A and 5B illustrate trajectory estimation being performed onfeature region pixels, according to one or more embodiments.

Referring to FIGS. 6A and 6B, in one or more embodiments, trajectoryestimation to a pixel of a current frame 640 from a feature region pixel601 of a previous frame 610, for example, may be performed. In one ormore embodiments, the trajectory estimation of a pixel of the currentframe 640 may be based only on feature region pixels of the previousframe 630, or only feature region pixels of previous frames. Brieflyonly an example, the current frame 640 may be considered frame(t) attime t, the previous frame 630 may be considered frame(t-1), frame 620may be considered frame(t-2), frame 610 may be considered frame(t-3), assimilarly shown in FIG. 6B. Accordingly, if the trajectory estimationbased on 10 frames is considered, for example as shown in FIG. 6B, anestimation of a pixel for frame(t) can ultimately be based on theestimation of a pixel for a frame(t-8) from a trajectory estimation of afeature region pixel in frame(t-9), . . . , estimation of a pixel offrame(t-2) based on a trajectory estimation of a feature region pixel inframe(t-3), estimation for a pixel of frame(t-1) based on a trajectoryestimation of a feature region pixel in frame(t-2), and estimation forthe pixel of frame(t) based on a trajectory estimation of a featureregion pixel in frame(t-1). As the information of the feature regionpixel of a previous frame, from which a subsequent frame's featureregion pixel was trajectory estimated, is known, pixel information froma lineage of related feature region pixels through several frames may beavailable, e.g., after the below discussed matching operation, foreither direct projection into a high definition frame or available forinterpolation pixels in the high definition frame.

Using a sub-pixel analysis, in one or more embodiments, and referring toFIG. 6A as an example, a feature region pixel 601 selected in frame 610may trace to a corresponding feature region pixel 602 in frame 620,e.g., potentially to a sub-pixel unit location in frame 620, theestimated feature region pixel 602 in frame 620 may trace to acorresponding feature region pixel 603 in frame 630 in a sub-pixel unit,and the estimated feature region pixel 603 may trace to a correspondingfeature region pixel 604 in frame 640 in a sub-pixel unit. Selection ofthe traced to feature region pixel in the frame 640 may be performed byselecting a pixel of frame 640 having a greatest correlation from amongseveral candidate pixels in frame 640, based on the trajectory of pixel603 in frame 630, e.g., with the pixel 604 being selected as having thegreatest correlation from among the several candidate pixels in frame640, for example. Accordingly, in such embodiments, trajectoryestimation for pixels of the current frame 640 may be performed onlywith respect to the feature region pixels 601, 602, and 603 of theprevious frames 610, 620, and 630, and trajectory estimation may notneed to be performed with respect to the remaining pixels of theprevious frames 610, 620, and 630, such as the uniform region pixels.

As will be discussed in greater detail below, according to one or moreembodiments, using respective trajectory information of a feature regionpixel as traced for each frame, e.g., through a lineage or relatedfeature region pixels, respective registration operations may beperformed to store information on a pixel value and location of thetrajectory estimated feature region pixel, e.g., in the registrationmemory, and potentially similar registration of pixel values andlocations of corresponding adjacent or neighboring pixels, andinformation of the feature region pixel from the previous frame fromwhich the trajectory estimated pixel was estimated.

For example, referring to FIG. 6B, positions of pixels adjacent to eachfeature region pixel of each of the frame(t-9) through frame(t-1) areillustrated. For each of the frames, in addition to the respectivefeature region pixels, positions of adjacent pixels, and a number ofused neighboring pixels, may vary based on accuracy of the registrationoperation, a complexity of a system, and the like.

In one or more embodiment, in the aforementioned trajectory estimationbased on feature region pixels from previous frames, for the previousframes only information of the respective feature region pixel andvalues of the corresponding adjacent pixels from each previous frame mayneed to be stored, compared to the aforementioned conventional imageprocessing techniques which require information of all pixels or blocksof each frame to be stored in a separate frame memory, i.e., withinformation of each frame being stored in a separate frame memory, andtrajectory estimation being required for all pixels or blocks from eachprevious frame. Further, as also noted above, in the conventional imageprocessing techniques, any frame registration of a current frame is onlyperformed after all movement estimations, which are required to be thesame as the number of reference frames, e.g., 10 estimations for eachmovement estimated pixel or block of a current frame, resulting in areal-time processing not being typically available. Conversely, in oneor more embodiments, limited pixel information from previous frames andonly a trajectory estimation from each feature region pixel may need tohave been stored for trajectory estimation pixels of the current frame.Further, in one or more embodiment, any of the previous trajectories fora corresponding feature region pixel may be updated each frame, suchthat trajectory estimation based on each of the feature region pixels ofeach of the previous frames can be performed in a real-time manner.Accordingly, in one or more embodiments, the trajectory estimation andinterpolation of feature region pixels of the feature region, theinterpolation of the uniform region, and the merging of the results ofboth interpolations are done in real-time.

Motion estimation would conventionally need to be performed on storedinformation of every pixel or block of a previous frame to tracemotion-estimated pixels into a current frame, and the motion estimationwould have to be performed for every previous frame that would be reliedupon for generating the high definition frame. However, herein, theinformation for previous related feature region pixels from previousframes, i.e., each trajectory estimating a feature region pixel in asubsequent frame, may be accessible for interpolation without themultiple levels of movement estimation for each of the availableprevious frame. Rather, by accessing the information of the featureregion pixel of the previous frame for trajectory estimated pixel of acurrent frame, the pixel information for an entire lineage of featureregion pixels may be available, e.g., from the registration memory.Accordingly, in one or more embodiments, substantially less memory maybe required to store information of only feature region pixels of eachprevious frame, and potentially corresponding adjacent or neighboringpixels, and respective updated trajectory information of feature regionpixels of previous frames frame, such that real-time processing can beperformed.

FIG. 7 illustrates trajectory estimation being performed on featureregion pixels at a sub-pixel unit, according to one or more embodiments.

Referring to FIG. 7, according to one or more embodiments, a pixel 701of a current frame 710 may be classified as a feature region pixel basedon pixel 701 resulting from trajectory estimation performed from afeature region pixel of a previous frame of the current frame 710, and asub-pixel 702 of the current frame 710 may also be classified as afeature region pixel based on pixel 702 also resulting from trajectoryestimation with respect to the feature region pixel of the previousframe of the current frame 710 up to a sub-pixel unit, e.g., theestimated pixel of the current frame may be a sub-pixel of the currentframe, i.e., located at a sub-pixel location between existing pixels ofthe current frame. Here, according to one or more embodiments, thedetermining of the feature region pixel of the current frame byperforming the trajectory estimation with respect to the feature regionpixel of the previous frame may include performing trajectory estimationup to the sub-pixel unit level. Information including an intensityvalue, a gradient value, a second differential value, a spatial spectrumvalue, and a pixel value of the sub-pixel 702 may be determined based oninterpolation from an adjacent pixel, for example. According to one ormore embodiments, the information including the intensity value, thegradient value, the second differential value, the spatial spectrumvalue, and the pixel value of the sub-pixel may be interpolated by aninteger unit to be determined.

FIG. 8 illustrates feature region pixels of frames of a low definitionvideo, and pixel values and locations of adjacent pixels, to be stored,according to one or more embodiments.

Referring to FIG. 8, according to one or more embodiments, trajectoryestimation may be performed for a pixel of a current frame 830 based ona feature region pixel 811 of a previous frame 810, e.g., a frame(t-2)or prior to two frames of the current frame 830. A pixel value andlocation of the pixel 811, as a feature region pixel of the previousframe 810, may be stored, e.g., in the aforementioned registrationmemory. As discussed above, the ‘pixel value’ of the feature regionpixel may be information including an intensity value, a gradient value,a second differential value, a spatial spectrum value, and a pixel valueof the feature region pixel, and the like. Here, the ‘location’ of thefeature region pixel may designate a location of the feature regionpixel in a frame of the low definition video, and according to one ormore embodiments, may be represented as coordinates, a vector, and thelike.

In addition, pixel values and locations of adjacent pixels 812 of thefeature region pixel 811, may also be stored, e.g., in the registrationmemory. The location of the adjacent (or neighboring) pixels may equallybe represented as coordinates or a vector, each having a relative valuewith respect to the location of the feature region pixel (e.g.,coordinates having a relative value based on the feature region pixel,or a vector represented as a distance and angle from the feature regionpixel), and also represented as coordinates, a vector, and the like,each having an absolute value (e.g., coordinates based on zero, or avector represented as a distance or angle from zero). As discussedabove, the number of neighboring pixels may be adjusted based onmatching accuracy and complexity of a system. For determining theadjacent or neighboring pixels, when the feature region pixel is a pixelof a sub-pixel unit, coordinates may be computed using a rounding-downoperation, and the adjacent or neighboring pixels may be determinedbased on the computed coordinates. For example, in a case of the featureregion pixel having coordinates (2, 2.5), coordinates (2, 2) may beobtained using the rounding-down operation, and pixels adjacent orneighboring the coordinates (2, 2) may be determined as the adjacent orneighboring pixel. According to one or more embodiments, the coordinatesmay also be computed using a rounding-up operation, or alternativeoperation.

The pixel value and location of the feature region pixel 821 of aprevious frame 820, e.g., frame(t-1) or prior to one frame, of a currentframe 830 of the low definition video, may equally be stored and pixelvalues and locations of an adjacent or neighboring pixels 822 of thepixel 821 may also be stored. Similarly, a pixel value and location ofthe classified feature region pixel 831 of the current frame 830, and apixel values and locations of adjacent or neighboring pixels 832 of thepixel 831 may also be stored, e.g., in the registration memory.

Thus, in one or more embodiments, trajectory estimated feature regionpixels of a low definition frame, such as current frame 830, may merelyrepresent the storing in such a registration memory of which featureregion pixels, and corresponding adjacent or neighboring pixels, forexample, from one or more previous frames would or should correspond topixels of the feature region in the low definition frame. As notedbelow, a matching operation may then proceed through these indicatedfeature region pixels from the previous frames based on theirinformation being stored in the registration memory, by moreparticularly matching pixels of the low definition frame with theindicated feature region pixels to actually generate the appropriatepixel values and transform the coordinates of the indicated featureregion pixels for projection to a high definition frame, with featureregion pixel information of the low definition frame also available forprojection to the high definition frame. Accordingly, based upon thetrajectory estimation and corresponding matching, there may besignificantly more pixels and pixel information available forinterpolating any one area of the high definition frame. The matchingmay also identify the pixel value of high definition frame directly fromone of the feature region pixels of the current frame or correspondingprevious frames. Conversely, in one or more embodiments, theinterpolation of the uniform region of the low definition frame may onlybe based on pixels within the uniform region, and thus the number ofpixels from which the interpolation can extrapolate a pixel value fromis limited.

Hereinafter, the matching of a feature region pixel of a current frameand a feature region pixel of a previous frame will be furtherdescribed.

FIG. 9 illustrates matching feature region pixels, according to one ormore embodiments.

Referring to FIG. 9, subsequent to the trajectory estimation, forexample, in one or more embodiments, a trajectory estimated featureregion pixel 941 of a current frame 940 may be matched to feature regionpixels 911, 921, and 931 of at least one of previous frames 910, 920,and 930 corresponding to the feature region pixel 941 of the currentframe 940, to thereby determine which pixel values from the previousfeature region pixels, and corresponding adjacent or neighboring pixels,should be used for interpolating pixels of the high definition videocorresponding to the current frame 940. Thus, after the trajectoryestimation, if a feature region pixel 911 is still found to adequatelymatch the feature region pixel 941, the pixel values of the featureregion pixel 911 and corresponding adjacent or neighboring pixels fromframe 910 may be directly projected into the high definition frameand/or used during the interpolation of pixels of the high definitionframe.

Thus, according to one or more embodiments, a pixel value and locationof the pixel 911 of the feature region pixel and a pixel value andlocation of adjacent pixels 912 (illustrated as diamonds) adjacent tothe pixel 911, with respect to a previous frame 910, e.g., a frame(t-3)or prior to three frames of the current frame 940, may have been stored,e.g., in the registration memory. Trajectory estimation may have beenperformed from a pixel of the previous frame 920, e.g., a frame(t-2) orprior to two frames of the current frame 940, to the feature regionpixel 911 of the previous frame 910, and a pixel value and location ofthe feature region pixel 921 of the previous frame 920 and a pixel valueand location of adjacent pixels 922 (illustrated as triangles) adjacentto the feature region pixel 921 may have been stored. Trajectoryestimation, from a pixel of the previous frame 930, e.g., a frame(t-1)or prior to one frame of the current frame 940, to the feature regionpixel 921 of the previous frame 920 may have been performed, and a pixelvalue and location of the estimated feature region pixel 931 of theprevious frame 930 and a pixel value and location of adjacent pixels 932(illustrated as squares) adjacent to the feature region pixel 931, mayhave been stored. Trajectory estimation, to a pixel of the current frame940, from the feature region pixel 931 of the previous frame 930, mayfurther be performed, and a pixel value and location of the estimatedfeature region pixel 941 and a pixel value and location of adjacentpixels adjacent to the feature region pixel 941 may be stored.

In one or more embodiments plural frames of the low definition video maybe input and trajectory estimation may not be performed until apredetermined number of frames have been input to the image processingsystem. For example, classification may still be performed on each lowdefinition frame in real-time, but not until a sufficient number offrames have been input is the trajectory estimation performed andcorresponding results stored. As only an example, a first input framemay only be classified into the feature region and uniform region, andtrajectory estimation would not be performed for the first input frame.Trajectory estimation and registration of the trajectory estimatedpixels, and corresponding adjacent or neighboring pixels, may ratherbegin with the second input frame. The first trajectory estimation andregistration of trajectory estimated pixels may similarly be performedat a later frame, and may further be performed for previous frames,e.g., if the predetermined number is 4, then four frames may be inputand sequentially classified into the uniform region or feature regionand upon input of the fourth frame trajectory estimation for the secondthrough third input frames may be performed, e.g., in parallel with theclassification of the currently input frame 4.

Accordingly, in the current frame 940, information about pixel valuesand locations with respect to the feature region pixels 911, 921, and931 of the previous frames 910, 920, and 930 and the adjacent pixels912, 922, 932, as well as pixel values and locations with respect to thefeature region pixel 941 of the current frame 940 may be available,e.g., from the registration memory, such that the respective previousadjacent pixels 912, 922, 932 could be projected onto the current frame940, or directly accessed from the registration memory by aninterpolation unit, such as the high resolution interpolation unit 1350of FIGS. 13A-13C. Thus, using this information about the pixel valuesand locations, a pixel value of each pixel of a frame of the highdefinition video corresponding to the current frame 940 may bedetermined to thereby produce the high definition video.

According to one or more embodiments, as shown in FIG. 9, the featureregion pixel 911 of the previous frame 910 may have coordinates (2, 2),and the adjacent pixel 912 may have relative coordinates (−1, 1) withrespect to the feature region pixel 911. The feature region pixel 921 ofthe previous frame 920 may have coordinates (2, 2.5), and the adjacentpixel 922 may have relative coordinates (−1, 0.5) with respect to thefeature region pixel 921. Also, the feature region pixel 931 of theprevious frame 930 may have coordinates (5.5, 1.5), and the adjacentpixel 932 may have relative coordinates (−0.5, −1.5) with respect to thefeature region pixel 931. In this instance, locations and pixel valuesof the adjacent pixels 912, 922, and 932 may be stored. The featureregion pixel 941 on which a trajectory estimation having been performedin the current frame 940 may have coordinates (4.5, 2).

According to one or more embodiments, the feature region pixel 941 ofthe current frame 940 may be matched with the feature region pixels 911,921, and 931 of the previous frames 910, 920, and 930, and potentiallywith the adjacent pixels 912, 922, and 932. Using the stored locationinformation indicating the adjacent pixel 912 is located at relativecoordinates (−1, 1) with respect to the feature region pixel 911, anadjacent pixel 942 of the current frame 940 may be located at relativecoordinates (−1, 1) with respect to the feature region pixel 941 of thecurrent frame 940, and thereby the adjacent pixel 942 may be determinedto have coordinates (3.5, 3). Similarly, a feature region pixel 943 ofthe current frame 940 may be determined to have coordinates (3.5, 2.5),and a feature region pixel 944 of the current frame 940 may bedetermined to have coordinates (4, 0.5). The updated pixel coordinatesfor the feature region pixels 911, 921, and 931 from the previous frames910, 920, and 930, and corresponding adjacent pixels 912, 922, and 932,may also be stored in the registration memory. Here, in one or moreembodiments, based upon all coordinates of the feature region pixelsbeing relative to the current frame 640, if a next low definition frameis input, and the trajectory estimation and interpolation describedabove is applied, potentially only the registration information of theprevious frame, i.e., the current frame 640, and updated respectivetrajectory information for the feature region pixels of the previousframes, may need to be reviewed to interpolate the next low definitionframe into a high definition frame.

Through the above described manner, the current frame 940 of the lowdefinition video may have a plurality of pixel values and locations ofadjacent pixels estimated with respect to the feature region pixel 941,based on select pixel values and locations from previous frames, and mayproduce an accurate high definition frame using the plurality of pixelvalues and locations of the adjacent pixels.

According to one or more embodiments, the aforementioned matchingoperation may selectively be performed only when a difference of a pixelvalue between a feature region pixel of a previous frame and a featureregion pixel of a current frame on which a trajectory estimation havingbeen performed fails to meet or is less than a reference value, or oneor more reference values. In this instance, when the difference issignificantly great, there may be no correlation between the featureregion pixel of the preceding frame and the feature region pixel of thecurrent frame, and the feature region pixel of the current frame may beunsuitable to be used for producing the high definition videocorresponding to the current frame. That is, in one or more embodiments,only when the difference of the pixel value therebetween fails to meetor is less than the reference value, may the matching operation beperformed, e.g., to prevent errors of the matching operation withrespect to the feature region pixel.

For example, when a difference between a pixel value (e.g., intensityvalue) of the feature region pixel 921 of the previous frame 920 and apixel value of the feature region pixel 941 of the current frame 940 ismore than the reference value, it may be determined that there is nocorrelation between the feature region pixel 921 and the feature regionpixel 941, and information about the feature region pixel 921 and theadjacent pixel 922 may be excluded when performing the matchingoperation, and as information used for producing the high definitionvideo corresponding to the current frame 940. Here, a high definitionframe may still be produced based on the remaining feature region pixelsand respective adjacent pixels of the previous frame 920. Thenon-matching of the feature region pixel 921 and adjacent pixel 922 maybe represented in the registration memory, either as an indication thatthe information of feature region pixel 921 and adjacent pixel 922should not be used in the interpolation operation or the information ofthe feature region pixel 921 and adjacent pixel 922 may merely not bestored at all in a corresponding portion of the registration memoryidentifying which feature region pixels and corresponding adjacentpixels or neighboring pixels can be utilized during the interpolationprocess.

According to one or more embodiments, the method may further includereceiving an input of one or more reference values for determining therequired correlation between feature region pixels of the current frameand respective feature region pixels of the previous frames for thismatching operation.

FIG. 10 illustrates a method of preventing errors from occurring in amatching of feature region pixels, according to one or more embodiments.

Referring to FIG. 10, in operation S1010, according to one or moreembodiments, a feature region pixel of a current frame may be furtherclassified into a texture region and an edge region.

According to one or more embodiments, the below discussed referencevalues Th_(texture) and Th_(edge) may be differently determineddepending on whether the feature region pixel of the current frame isrespectively included in either the texture region or the edge region.Errors of the matching operation may not be easily recognized when thefeature region pixel is included in the texture region, whereasdeterioration in image quality may be easily recognized due to theerrors of the matching operation in a boundary when the feature regionpixel is included in the edge region. Accordingly, in one or moreembodiments, reference value Th_(texture) may be used to correct orprevent the errors of the matching operation in the texture region, andTh_(edge) may be used to correct or prevent the errors of the matchingoperation in the edge region. In one or more embodiments, theTh_(texture) and Th_(edge) reference values may be reference values ofbrightness values of registration error of the texture region and theedge region, respectively. Generally, in one or more embodiments,Th_(texture) may be determined to be higher than Th_(edge).

Accordingly, when the feature region pixel of the current frame isclassified into the texture region, in operation S1020, there may be adetermining of whether a difference between a pixel value between thefeature region pixel of the texture region and a feature region pixel ofa previous frame corresponding to the feature region pixel is less thanthe reference value of Th_(texture), for example. When the difference ofthe pixel value therebetween is less than Th_(texture), in operationS1021, the above discussed matching operation may be performed betweenthe feature region pixel of the texture region and the feature regionpixel of the previous frame. However, when the difference of the pixelvalue therebetween exceeds Th_(texture), in operation S1022, the featureregion pixel of the previous frame may be excluded when performing theaforementioned matching operation, and similar to above, informationregarding the feature region pixel may not be used in the interpolationoperation, e.g., according to storage/non-storage of the feature regionpixel of the previous frame in the registration memory, or portion ofthe registration memory that is controlling of which pixel informationis used in interpolating pixels of the high definition framecorresponding to the feature region.

When the feature region pixel of the current frame is classified intothe edge region, in operation S1030, it may be determined whether adifference of a pixel value between the feature region pixel of the edgeregion and a feature region pixel of a previous frame corresponding tothe feature region pixel is less than the reference value of Th_(edge).When the difference of the pixel value therebetween is less thanTh_(edge), in operation S1031, the above discussed matching operation ofthe feature region pixel of the edge region and the feature region pixelof the previous frame may be performed. However, when the difference ofthe pixel value therebetween exceeds Th_(edge), in operation S1032, thefeature region pixel of the previous frame may be excluded whenperforming the aforementioned matching operation, and similar to above,information regarding the feature region pixel may not be used in theinterpolation operation, e.g., the feature region pixel of the previousframe may not be stored in the registration memory according tostorage/non-storage of the feature region pixel of the previous frame inthe registration memory as pixel information to be accessed duringinterpolation of pixels of the high definition frame corresponding tothe feature region. According to one or more embodiments, toadditionally prevent errors of the matching operation, an edgepreserving low pass filter may be applied to the feature region pixel ofthe edge region, as illustrated in FIG. 13C. The low pass filter foredge protection, may further attempt to reduce registration error.

FIG. 11 illustrates a frame of a high definition video corresponding toa frame of a low definition video, according to one or more embodiments.As illustrated in FIG. 11, the illustrated pixels have been localized tothe coordinate system of the current frame, i.e., the low definitionframe of the low definition video, based on the above trajectoryestimation, feature region pixel classification, and matching, as onlyexamples. The localized coordinates may equally be obtained by aninterpolation unit, such as the high resolution interpolation unit 1350of FIGS. 13A-C, for example, merely from the registration memory, orthey may be projected onto the current frame as shown in FIG. 11.

Referring to FIG. 11, a current frame 1110 of the low definition video,according to an embodiment, may have feature region pixels 1111, 1112,1113, 1114, 1115, 1116, and 1117. The feature region pixel 1111 may havecoordinates (0, 0), the feature region pixel 1112 may have coordinates(1, 0.5), the feature region pixel 1113 may have coordinates (3, 1), thefeature region pixel 1114 may have coordinates (1.5, 1.5), the featureregion pixel 1115 may have coordinates (1, 2), the feature region pixel1116 may have coordinates (3, 2), and the feature region pixel 1117 mayhave coordinates (2, 3). Here, the coordinates are based upon sub-pixelunits the current frame 1110, resulting in some pixels having halfx-axis or y-axis coordinates, for example. Pixel values and locations,for example, of the feature region pixels 1111, 1112, 1113, 1114, 1115,1116, and 1117 may be stored, as noted above. Similar to theillustration in FIG. 9 of adjacent pixels of feature region pixel 911being represented with diamonds, triangles, and squares, representingthe different frames including the respective adjacent pixels, FIG. 11illustrates projected trajectory estimated feature region pixels, orclassified feature region pixels being represented from differentprevious (or current) frames according to a diamond representing featureregion pixel 1113, triangles representing feature region pixels 1115 and1117, a square representing feature region pixel 1111, dotted circlerepresenting pixel 1114, and the circle within a circle representingpixel 1116.

Accordingly, as noted, in one or more embodiments, the current frame1110 of the low definition video may be a frame including a featureregion pixel of the current frame 1110 and at least one feature regionpixel corresponding to one or more previous frames of the current frame1110, i.e., the feature region pixels of the previous frame may beprojected onto the current frame 1110. In one or more embodiments, thefeature region pixels 1111, 1112, 1113, 1114, 1115, 1116, and 1117 couldalternatively be adjacent pixels of the feature region pixels of thecurrent frame 1110 and/or adjacent pixels of the at least one featureregion pixel of the previous frame projected onto the current frame1110.

During the interpolation, when a high definition frame corresponding tothe current frame 1110 of the low definition video is produced to havetwice the resolution in comparison with that of the low definitionvideo, feature region pixels 1121, 1122, 1123, 1124, 1125, 1126, and1127 of a frame 1120 of the high definition video, corresponding to thefeature region pixels 1111, 1112, 1113, 1114, 1115, 1116, and 1117 ofthe current frame 1110 of the low definition video, may have coordinatescorresponding to those of the feature region pixels 1111, 1112, 1113,1114, 1115, 1116, and 1117, with each x- and y-axis coordinatemultiplied by two. That is, in the high definition frame 1120 of thehigh definition video, the feature region pixel 1121 of the frame 1120may now have coordinates (0, 0), the feature region pixel 1122 may nowhave coordinates (2, 1), the feature region pixel 1123 may now havecoordinates (6, 2), the feature region pixel 1124 may now havecoordinates (3, 3), the feature region pixel 1125 may now havecoordinates (2, 4), the feature region pixel 1126 may now havecoordinates (6, 4), and the feature region pixel 1127 may now havecoordinates (4, 6).

A pixel value of a pixel 1128, which is different from the featureregion pixels 1121, 1122, 1123, 1124, 1125, 1126, and 1127, may beobtained by interpolation, noting that the pixel values corresponding toany of the feature region pixels 1121, 1122, 1123, 1124, 1125, 1126, and1127 in the high definition frame 1120 may equally be based on furtherinterpolation operations. FIG. 12 below further discussed such aninterpolation operation for pixel 1128, for example.

FIG. 12 illustrates performance of an interpolating of pixels of a frameof a high definition video, according to one or more embodiments.

Referring to FIG. 12, a frame 1210 of the high definition video may havea feature region pixel 1211 corresponding to a feature region pixel of alow definition video, and an interpolated pixel 1212, illustrated inFIG. 12 as the white pixel 1211. As illustrated in FIG. 12, the featureregion pixel 1211 may already have a pixel value based upon thetrajectory estimation. However, a pixel value of the white pixel 1212may need to be obtained by interpolation.

Taking a correlation between the feature region pixel 1211 of the frame1210 of the high definition video and the white pixel 1212 into account,the pixel value of the white pixel 1212 may be interpolated by:

${I_{out}\left( {x,y} \right)} = {\sum\limits_{i = 1}^{n}{w_{i}I_{i}}}$

Here, w_(i) denotes a weight. In one or more embodiments, the weightwhich may be represented as:

$w_{i} = \frac{d_{i}^{- p}}{\sum\limits_{i = 1}^{n}d_{i}^{- p}}$

Here, d_(i) denotes a distance between a black pixel 1211 and the whitepixel 1212, and p denotes a constant value controlling a magnitude ofthe distance. That is, the interpolation with respect to a pixel valueof a frame of the high definition video may be performed baseddistance(s) between the to-be-interpolated pixel and near feature regionpixels and potentially respective an adjacent pixel(s), acting as theweight. As shown in illustration 1220, the illustrated differentdistances between the white pixel 1211 and the near feature regionpixels or respective adjacent pixels can be used to interpolate thepixel value of the white pixel 1211. The number and/or proximity offeature region pixels, and potentially corresponding adjacent orneighboring pixels, that are used for such an interpolating of the whitepixel 1211 may be predefined or varied, e.g., based on availableresources. Additionally, the interpolation of the white pixel 1211 mayalso be based on already interpreted pixels of the high definitionframe.

Hereinafter, a producing of a high definition video from the lowdefinition video, according to one or more embodiments will be furtherdescribed.

In one or more embodiments, trajectory estimation with respect to afeature region pixel of an (i−1)-th frame (equated with theaforementioned frame(t-1)) of the low definition video may be performedto thereby determine a feature region pixel of an i-th frame (similarlyequated with the aforementioned frame(t)) of the low definition video.

A feature region pixel of the i-th frame and at least one feature regionpixel of a previous frame corresponding to the feature region pixel ofthe i-th frame may then be matched to determine pixel values of pixelsof a frame of the high definition video corresponding to the i-th frame.

Accordingly, using interpolation, pixel values may be interpolated forpixels of the corresponding high definition frame that have not beendetermined, such as pixels between the feature region pixels from thelow definition frame, between feature region pixels from previous lowdefinition frames projected onto the low definition frame, or pixelsthat are overlapping, for example.

According to one or more embodiments, the feature region pixel of the(i−1)-th frame may have been trajectory-estimated from a feature regionpixel of an (i−2)-th frame of the low definition video, or may have beena pixel within the (i−1)-th frame having a relatively high spatialspectrum. That is, a pixel of the (i−1)-th frame having beentrajectory-estimated from a pixel determined as the feature region pixelin the (i−2)-th frame may also have been classified as a feature regionpixel in the (i−1)-th frame, and remaining pixels of the (i−1)-th framemay have been classified into either of the feature region and theuniform region in accordance with their respective spatial spectrum, asdiscussed above.

Similarly, according to one or more embodiments, the feature regionpixel of the (i−2)-th frame may have been trajectory-estimated from afeature region pixel of an (i−3)-th frame of the low definition video,or may have been a pixel within the (i−2)-th frame having a relativelyhigh spatial spectrum. That is, a pixel of the (i−2)-th frame havingbeen trajectory-estimated from a pixel determined as the feature regionpixel in the (i−3)-th frame may also have been classified as a featureregion pixel in the (i−2)-th frame, and remaining pixels of the (i−2)-thframe may have been classified into either of the feature region and theuniform region in accordance with their respective spatial spectrum, asdiscussed above.

Accordingly, the classification of pixels of a current frame as one ofthe feature region and the uniform region may be performed based onfeature region pixels from at least one previous frame being trajectoryestimated into the current frame, as feature region pixels of thecurrent frame, and with the remaining pixels of the current frame beingclassified into either of the feature region or uniform region based ontheir respective spatial spectrum. A high definition frame can beproduced from the low definition current frame by applying the knownpixel information of the classified feature region pixels of the currentframe and the known pixel information of the previous frames' featureregion pixels, and potentially adjacent or neighboring pixels, e.g.,based upon a selective matching operation, localizing the coordinates ofthe known pixel information from previous frames projecting the knownpixel information for previous frames and the current frames into thehigh definition frame, and interpolating the pixels that remain to bedetermined in the high definition frame. In an embodiment, theinterpolation may equally include interpolating the known pixelinformation of the feature region pixels, such as when two or more areestimated to be overlapping or are in close proximity in the highdefinition frame, such by the above described distance basedinterpolation with regard to the unknown pixels of the high definitionframe.

The above reference to embodiments regarding a conversion of thedefinition or resolution of a current frame of a video, e.g., a lowdefinition video being converted into a high definition video, areequally applicable to a single image being converted to a highdefinition or resolution image based upon additional information, suchas pixel information and trajectory information of another or previousimage, though the additional information is not required to be fromanother or previous image and may be information from a previouscompression of the current image into a low definition image, notingthat alternatives are equally available. Alternatively, one or moreembodiments include a system and method compressing a high definitionimage based on a classification of the high definition image into one ofthe feature and uniform regions and a generated trajectory informationfor pixels classified into the feature region, based upon the aboveavailable classification operations, and a corresponding decompressingof the compressed low definition image based on the generated trajectoryinformation and positions and values of feature region pixels, andpotentially adjacent or neighboring pixels, corresponding to the lowdefinition image, to thereby reproduce the high definition image basedupon one or more of the described embodiments described herein.

Hereinafter, a method of producing the high definition video from thelow definition video, according to one or more embodiments, will befurther described.

First, a first frame of the low definition video may be input.

In an embodiment, the first frame may be divided into N×M-numberedblocks. In one or more embodiments, by adjusting a magnitude of N or M,a number of feature region pixels of the first frame may be adjusted.

Any, alone or in combination, of the aforementioned operations forclassifying pixels of the first frame into either of the feature regionand the uniform region may be performed, and pixel values and locationsof the feature region pixels of the first frame may be stored. Also, thepixel value and location of pixels adjacent to the classified featureregion pixels of the first frame may be stored. Similarly, in one ormore embodiments, if the first frame has been divided into theN×M-numbered blocks the performing of the classifying of pixels of thefirst frame into either of the feature region and the uniform region mayclassify only a single pixel for each block as being a feature regionpixel, thereby representing all pixels of the block, if at least onepixel of that block would be determined to be feature region pixelaccording to the aforementioned operations. The feature region pixel ofthe block may be a pixel of the block that meets the most criteria orhas the greatest value relative to the respective reference values forthe above mentioned classifying operations, as only an example. Theremaining pixels of the same block as the classified feature regionpixel may be classified as adjacent or neighboring pixels of therespective feature region pixel. Blocks of which no pixel would bedetermined to be a feature region pixel according to one or more of theaforementioned operations may be classified into the uniform region,along with all pixels of the respective blocks being classified into theuniform region.

Additionally, a second frame of the low definition video may be input.

According to one or more embodiments, the second frame may be dividedinto N×M-numbered blocks.

A trajectory estimation may be performed from respective feature regionpixels of the first frame to thereby estimate a pixel in the secondframe corresponding to the feature region pixel of the first frame. Asdiscussed above, pixels that have been classified as uniform regionpixels in the previous frame are not required for estimation of pixelsof the feature region of the second frame, and thus trajectoryestimation of only the feature region pixels of the first frame may beperformed.

Additionally, according to one or more embodiments, the ‘trajectoryestimation’ may include selecting a pixel having the highest correlationwith a feature region pixel of the first frame and a pixel of the secondframe, using information including an intensity value, a gradient value,a second differential value, a spatial spectrum value, and a pixel valueof a pixel, and the like, as well as an SAM method of performing thetrajectory estimation using an intensity difference, such as when thefirst or second frames have been divided into N×M-numbered blocks, asonly examples.

According to one or more embodiments, the method may perform thetrajectory estimation up to a sub-pixel unit in the first frame tothereby determine a sub-pixel of the second frame as the trajectoryestimated feature region pixel. Information including an intensityvalue, a gradient value, a second differential value, a spatial spectrumvalue, and a pixel value of a pixel of the sub-pixel unit of the secondframe may further be determined from one or more adjacent pixels usinginterpolation, as only examples.

In a case where there is a transition, occlusion, and the like detectedor expected, the trajectory estimation may not be performed with respectto a pixel of the second frame corresponding to the feature region pixelof the first frame. In this case, there may be no pixel of the secondframe corresponding to the feature region pixel of the first frame, andthe pixel that would have been defined by the feature region pixel ofthe first frame may need to be represented by another pixel near thefeature region pixel, e.g., another pixel within the same block when thefirst frame has been divided into N×M-numbered blocks.

Accordingly, according to one or more embodiments, a pixel of the secondframe on which the trajectory estimation has been performed may beclassified as a feature region pixel of the second frame, and a pixelvalue and location of the feature region pixel may be stored. Pixelvalues and locations of one or more adjacent pixels of the featureregion pixel of the second frame may also be stored. Further, pixelvalues and locations of pixels of the second frame that have beenclassified into the feature region, though not estimated throughtrajectory estimation, may equally be stored, and pixel values andlocations of one or more corresponding adjacent pixels may also bestored. Similar to above, if the second frame has been divided into theN×M-numbered blocks, the pixel values and locations of the remainingpixels of a block may be stored in addition to pixel value and locationof the feature region pixel representing the block.

Thereafter, an i-th frame of the low definition video may be input.

According to one or more embodiments, the i-th frame may be divided intoN×M-numbered blocks

Trajectory estimation from a pixel classified as a feature region pixelin an (i−1)-th frame, such as the second frame when i equals 2, may thenbe performed to estimate pixels of the i-th frame corresponding to thefeature region pixel of the (i−1)-th frame. The trajectory estimationmay be performed up to a sub-pixel unit in the (i−1)-th frame to therebyestimate a sub-pixel of the i-th frame as the feature region pixel.

Thus, pixel values for respect to pixels of which pixel values have notbeen determined may be interpolated in a high definition frame of thehigh definition video corresponding to the i-th frame.

A frame of the high definition video corresponding to the i-th frame ofthe low definition video, e.g., with the estimated pixels andinterpolated pixels, may thus be output. Similarly, frames such as an(i+1)-th frame, an (i+2)-th frame, and the like of the high definitionvideo may be produced in real time.

Additionally, high definition video corresponding to plural highdefinition frames may be produced, e.g., by the high resolutioninterpolation unit 1350 of FIGS. 13A-C. The respective pixels in theuniform region of each frame may be projected to a high definition framecorresponding to the uniform region, e.g., by merely multiplying theirx- and y-axes coordinates by a value. In one or more embodiments, theunknown pixels within the high definition frame corresponding to theuniform region may be estimated, e.g., with the interpolation of pixelvalues within the high definition frame being based only on the pixelsfrom the same low definition frame that were projected onto the highdefinition frame corresponding to the uniform region.

In an embodiment, the interpolation for pixels in the high definitionframe corresponding to the uniform region may be limited tointerpolating pixels between the pixels classified into the uniformregion that would not overlap pixels of a high definition framecorresponding to the feature region, and similarly the interpolation forpixels in the high definition frame of the feature region pixels may belimited to interpolating pixels that would not overlap pixels of thehigh definition frame corresponding to the uniform region, such that amerging of the high definition video based on the feature region and thehigh definition of the video based on the uniform region may be easilycombined without further substantial interpolation. Alternatively,additional interpolations may be implemented for an overlap between thehigh definition frame corresponding to the feature region and the highdefinition frame corresponding to the uniform region. Likewise,interpolation may be implemented if a high definition videocorresponding to the feature region is merged with a high definitionvideo corresponding to the uniform region.

Accordingly, the high definition video corresponding to the featureregion pixels and the high definition video corresponding to the uniformregion pixels may then be merged, and a single high definition video maybe output representing all portions of the input low definition video.

In one or more embodiments, in producing a high definition frame, apixel estimating and/or an alternative interpolation technique forpixels corresponding to the uniform region of the low definition frame,may be used to produce the high-definition frame based on the uniformregion, as long as the necessary processing and/or memory requirementsfor the pixel estimation and/or interpolating technique(s) of pixelsclassified into the uniform region are less and different from theestimation and/or interpolation process of the feature region, basedupon those pixels of the uniform region being classified as beingdifferent from pixels of the feature region. In one or more embodiments,the producing of the high definition frame corresponding to the uniformregion is based on there being no estimation operation for additionalpixels of the low definition frame, and only an interpolation of pixelsbetween the existing pixels classified into the uniform region, i.e.,the high definition frame corresponding to the uniform region is derivedfrom only pixel information provided within the uniform region of thelow definition frame. Additionally, the pixels of each of the featureregion and/or uniform region for each frame may equally be furtherclassified into respective sub-regions, where selective estimationand/or interpolation may be performed. For example, though embodimentsmay be described as applying the same estimation and interpolationtechnique for all pixels within the feature region and the sameinterpolation technique for all pixels within the uniform region,respective estimation and/or interpolation techniques for pixels of thefeature region and uniform region may be selectively different basedupon additional factors, such as the location of each pixel within thecurrent frame or potentially defined regions of interest, for example.

FIGS. 13A-C illustrate image processing systems producing a highdefinition image from a low definition image, according to one or moreembodiments. According to one or more embodiments, the image processingsystems of FIGS. 13A-C perform one or more of the embodiments describedabove.

As only examples, with reference to FIGS. 13A-B, the regionclassification unit 1320 may classify pixels of the low definition frameinto the feature region and the uniform region, and store information ofthe classified feature region pixels, and neighboring pixels, in aregistration memory. The classified uniform region pixels may also bestored, e.g., if the high resolution interpolation unit 1350 does notdirectly access the identified uniform region pixels from the lowdefinition frame. Referring to FIG. 13C, the separately illustratedregion classification units 1320 may be a single unit that performsclassifications into both the feature region and the uniform region, orthey may be separate units that individually classify pixels into thefeature region and the uniform region. The arrangement of the pluralregion classifications 1320 of FIG. 13C is equally available to theembodiments of FIGS. 13A-B.

Additionally, the region classification unit 1320 may perform the abovedescribed division of the current frame into the N×M-numbered blocks,and respective classification of the same, and/or the review of minimaldistances between feature region pixels as controlling of whether acurrent pixel is classified into the feature region or the uniformregion, as only examples. The region classification unit 1320 may alsocontrol a minimal distance between feature region pixels, andselectively classify pixels within the minimal distance.

Referring to FIGS. 13A-C, the trajectory estimation unit 1330 mayperform trajectory estimation of feature region pixels from previousframe(s) to the classified feature region pixels of the low definitionframe and selectively store their trajectory estimated positions in theregistration memory, potentially with the selective storing of thetrajectory estimated positions in the registration memory furtherincluding a matching operation of the trajectory estimated featureregion pixels of the low definition frame and the corresponding featureregion pixels of the previous frame(s). The trajectory estimation oftrajectory estimation unit 1310 may operate similarly to the describedtrajectory estimation unit 1330.

As illustrated in FIG. 13B, in one or more embodiments, a trajectoryestimation may be performed by an trajectory estimation unit 1310 beforethe classifying of the pixels into the feature region or the uniformregion by the region classification unit 1320, and any resultingtrajectory estimated pixels of the low definition frame may beclassified as feature region pixels of the low definition frame and aclassification of only the remaining pixels into the feature region oruniform region would be performed by the region classification unit1320, i.e., the trajectory estimated pixel may automatically beclassified into the feature region as it was estimated from a featureregion pixel of a previous frame. Information of the trajectoryestimated pixel may be stored. Accordingly, the region classificationunit 1320 may not perform any operations on the trajectory estimatedpixel and merely proceed with classifying a next pixel. In thisembodiment, the trajectory estimation unit 1310 is positioned before theregion classification unit 1320, and another trajectory estimation unit1330 is shown following the region classification unit. Here, thetrajectory estimation unit 1310 and the trajectory estimation unit 1330may be the same unit, they may perform different duties, such as thetrajectory estimation unit 1330 potentially updating a trajectory, orthe trajectory estimation unit 1330 may not be needed. In the embodimentof FIG. 13B, the trajectory estimation unit 1310 may not be implementedwhen trajectory information is not available, such as when the inputframe is a first frame of a series of frames and there is no previousframe information available. However, when the next frame is input, thetrajectory estimation unit 1310 may be operated, as trajectoryinformation from the previous frame may now be available.

Once identified, pixel values and locations of the trajectory estimatedpixel of the low definition frame, and adjacent or neighboring pixels,as well as the pixel value and location of the particular feature regionpixel of the previous frame whose trajectory estimated the trajectoryestimated pixel of the low definition frame may be used for matching thefeature region pixels of the current frame with feature region pixels ofseveral previous frames. By knowing the particular feature pixel of theprevious frame, corresponding feature region pixels from all previousframes may also be available. For example, if the feature region pixelof the previous frame was also a result of trajectory estimation, from afeature region pixel of a further previous frame, the pixel values andlocations of feature region pixel of the further previous frame may havebeen previously stored. Based upon the trajectory estimation of thepixel in the low definition frame, from the feature region pixel of theprevious frame, a related lineage of previous feature region pixels fromprevious frames may be know. Based upon this known lineage, pixelinformation from several previous frames can be easily projected to anarea of the high definition frame merely by accessing the storedinformation of these feature region pixels and their trajectoryinformation, e.g., identifying which feature region pixel of a previousframe they were estimated from.

The trajectory estimation unit 1310 in FIG. 13B and trajectoryestimation units 1330 in FIGS. 13A-C may selectively not perform thetrajectory estimation based upon a detected transition, occlusion, andthe like. The trajectory estimation unit 1310 or trajectory estimationunits 1330 may also be incorporated with the region classification unit1320. Further, in one or more embodiments, the trajectory estimationunit 1330 may only perform trajectory estimation on feature regionpixels, i.e., pixels that are classified into the feature region by theregion classification unit 1320.

The registration unit 1340 may control which pixel value information isultimately available for interpolation, and further may organize thefeature region pixel information and respective trajectories forsubsequent frame conversions. For example, the registration unit 1340may perform the above matching operation. The registration unit 1340 ofFIG. 13C is illustrated as including at least a texture registrationunit 1342, an edge registration unit 1344, and a low pass filter 1346.The operations of the texture registration unit 1342, edge registrationunit 1344, and low pass filter 1346 are the same as those discussedabove.

Similar to the registration process discussed above regarding FIG. 10,the registration unit 1340, and potentially texture registration unit1342, edge registration unit 1344, and low pass filter 1346, includedwith the registration unit 1340, may limit the matching based on acomparison between respective feature region pixels of the previousframe and trajectory estimated pixels of the current frame. In one ormore embodiments, respective matching may proceed based on a featureregion pixel of a previous frame according to a comparison of pixelvalue of the feature region pixel of the previous frame stored in amemory, such as the registration memory, and a brightness of thetrajectory estimated pixel of the current frame, for example. In one ormore embodiments, the registration unit 1340 of FIG. 13A, as only anexample, may perform this a comparison.

The high resolution interpolation unit 1350 of FIGS. 13A-C may produce ahigh definition frame corresponding to the feature region and a highdefinition frame corresponding to the uniform region pixels, and mergethe two high definition frames together to generate the high definitionvideo. The high resolution interpolation unit 1350 may alternativelyproduce separate high definition videos for the feature region and theuniform region according to their respective generated frames, and mergethe separate high definition videos.

Similar to above, and again referring to FIGS. 13A-C, in one or moreembodiments, the high resolution interpolation unit 1350 may directlyproject feature region pixels from previous frames, and potentiallyrespective adjacent or neighboring pixels, based on storage of pixelinformation of feature region pixels of previous frames in theregistration memory, e.g., as described in the above matching operation,and stored pixel information of feature region pixels of the lowdefinition frame, and potentially respective adjacent or neighboringpixels, that have been classified into the feature region in the lowdefinition frame. The high resolution interpolation unit 1350 mayfurther interpolate the remaining pixel values for the high definitionframe corresponding to the feature region based on the feature regionpixels from the previous frames, respective adjacent or neighboringpixels, and the classified feature region pixels, and respectiveadjacent or neighboring pixels, of the low definition frame. Theregistration memory may be external to the image processing systems orincluded in any of the trajectory estimation unit 1310, the regionclassification unit 1320, the trajectory estimation unit 1330,registration unit 1340, or high resolution interpolation unit 1350. Thehigh resolution interpolation unit may selectively access theregistration memory when interpolating pixels in the high definitionframe corresponding to feature region pixels of the low definition frameand previous frames. The production of the high definition framecorresponding to the feature region and the high definition framecorresponding to the uniform region may be produced in parallel.

Further, the high resolution interpolation unit 1350 may merge theseparately produced high definition frames respectively corresponding tothe feature region and the uniform region, merge high definition videosproduced by a respective sequencing of each of plural high definitionframes corresponding to the feature region and a sequencing of each ofplural high definition frames corresponding to the uniform region, e.g.,as respectively generated from plural corresponding frames of a lowdefinition video input to the image processing system. In one or moreembodiments, the high resolution interpolation unit 1350 may merelyperform the interpolation of a single high definition frame differentlybased upon registration information, provided from trajectoryestimation, classification, and matching for a feature region of aninput low definition frame and previous low definition frame(s), forexample, and pixel information for pixels within the uniform region ofthe input low definition frame, e.g., such that a complete highdefinition frame is generated from pixel information derived for thefeature region and pixel information from a uniform region, e.g.,without producing a high definition feature region frame and a highdefinition uniform region frame. Plural single high definition framesmay then be merged to generate the high definition video.

In the image processing system of FIG. 13B, as only an example, each ofthe trajectory estimation unit 1310, region classification unit 1320,trajectory estimation unit 1330, registration unit 1340, and highresolution interpolation unit 1350 may have respective buffers, or theremay be a single buffer for all units, and information from these buffersmay selectively be provided to a registration memory, or a part of theregistration memory that is looked to by the high resolutioninterpolation unit 1350 during an interpolation operation for pixels forthe high definition frame corresponding to the feature region.

For example, feature region pixels from the previous images that arematched by the above matching operation may be identified in theregistration memory, and potentially corresponding adjacent orneighboring pixels, such that the high resolution interpolation unit1350 of FIGS. 13A-C selectively chooses the appropriate storedinformation and performs the interpolation of pixels within the highdefinition frame corresponding to the feature region.

Referring to FIGS. 13A-C, if a compressed image is input, e.g., of ahigh definition image that has been compressed based on a classificationof the high definition image into either of the feature and uniformregions and a generated trajectory information for pixels classifiedinto the feature region, based upon one or more of the above availableclassification operations, the compressed image may be decompressed to arestored high definition or resolution image based upon input trajectoryinformation and positions and values of feature region pixels, andpotentially corresponding adjacent or neighboring pixels, as defined bythe compressing of the high definition image, to thereby reproduce thehigh definition image based upon one or more of the above describedembodiments. For example, the defined information may be provided to theregistration memory of FIGS. 13A-C, region classification unit 1320 mayperform region classification of the input image, the trajectoryestimation units 1310 and 1330 may perform trajectory estimation basedupon the provided information stored in the registration memory, theregistration unit 1340 (and potentially texture registration unit 1342,edge registration unit 1344, and low pass filter 1346) may perform aregistration operation of the image, e.g., such as a selective matchingof the feature region pixels of the input image and feature region pixelinformation stored in the registration memory, and the high resolutioninterpolation unit 1350 may generate the restored high definition imagefrom interpolated pixel information of the uniform region andinterpolated pixel information of the feature region.

In one or more embodiments, apparatus, system, and unit descriptionsherein include one or more hardware processing elements. For example,each described unit may include one or more processing elementsperforming the described operation, desirable memory, and any desiredhardware input/output transmission devices. Further, the term apparatusshould be considered synonymous with elements of a physical system, notlimited to a single enclosure or all described elements embodied insingle respective enclosures in all embodiments, but rather, dependingon embodiment, is open to being embodied together or separately indiffering enclosures and/or locations through differing hardwareelements.

In addition to the above described embodiments, embodiments can also beimplemented through computer readable code/instructions in/on anon-transitory medium, e.g., a computer readable medium, to control atleast one processing device, such as a processor or computer, toimplement any above described embodiment. The medium can correspond toany defined, measurable, and tangible structure permitting the storingand/or transmission of the computer readable code.

The media may also include, e.g., in combination with the computerreadable code, data files, data structures, and the like. One or moreembodiments of computer-readable media include magnetic media such ashard disks, floppy disks, and magnetic tape; optical media such as CDROM disks and DVDs; magneto-optical media such as optical disks; andhardware devices that are specially configured to store and performprogram instructions, such as read-only memory (ROM), random accessmemory (RAM), flash memory, and the like. Computer readable code mayinclude both machine code, such as produced by a compiler, and filescontaining higher level code that may be executed by the computer usingan interpreter, for example. The media may also be a distributednetwork, so that the computer readable code is stored and executed in adistributed fashion. Still further, as only an example, the processingelement could include a processor or a computer processor, andprocessing elements may be distributed and/or included in a singledevice.

The computer-readable media may also be embodied as at least oneapplication specific integrated circuit (ASIC) or Field ProgrammableGate Array (FPGA), which executes (processes like a processor) programinstructions.

While aspects of the present invention has been particularly shown anddescribed with reference to differing embodiments thereof, it should beunderstood that these embodiments should be considered in a descriptivesense only and not for purposes of limitation. Descriptions of featuresor aspects within each embodiment should typically be considered asavailable for other similar features or aspects in the remainingembodiments. Suitable results may equally be achieved if the describedtechniques are performed in a different order and/or if components in adescribed system, architecture, device, or circuit are combined in adifferent manner and/or replaced or supplemented by other components ortheir equivalents.

Thus, although a few embodiments have been shown and described, withadditional embodiments being equally available, it would be appreciatedby those skilled in the art that changes may be made in theseembodiments without departing from the principles and spirit of theinvention, the scope of which is defined in the claims and theirequivalents.

1. An image processing method, the method comprising: performingtrajectory estimation on pixels or blocks of a current frame, includingpixels or blocks corresponding to a feature region of the current frame,to identify trajectory estimated pixels when trajectory information of aprevious frame is available; classifying the trajectory estimated pixelsor blocks into a feature region and individually classifying pixels orblocks of the current frame, excluding the trajectory estimated pixelsor blocks, respectively into one of the feature region and a uniformregion in accordance with each respective spatial spectrum; matchingfeature region pixels or blocks of the feature region with featureregion pixels or blocks of at least one previous frame, and projectingmatched feature region pixels or blocks of the at least one previousframe and the corresponding trajectory estimated pixels or blocks into ahigh definition frame having a higher definition than the current frame;and interpolating pixels of the high definition frame, excluding theprojected matched feature region pixels or blocks and the correspondingtrajectory estimated pixels or blocks, based on the matched featureregion pixels or blocks and the corresponding trajectory estimatedpixels or blocks, and based on pixels or blocks of the uniform region.2. A method of producing a high definition video from a low definitionvideo, the method comprising: individually classifying pixels or blocksof a current frame of the low definition video into a feature region anda uniform region in accordance with a respective spatial spectrum ofeach pixel or block, with pixels or blocks classified into the featureregion respectively being feature region pixels or blocks; and producinga high definition video corresponding to the feature region based on aplurality of frames of the low definition video with respect to thefeature region and based on pixel value information from at least oneprevious frame to the current frame, and producing a high definitionvideo corresponding to the uniform region using interpolation at leastbetween pixels or blocks of the current frame classified into theuniform region.
 3. The method of claim 2, wherein the producing of thehigh definition video corresponding to the feature region includesdetermining pixel values of pixels of a frame of the high definitionvideo corresponding to the feature region by matching feature regionpixels of the current frame with feature region pixels of the at leastone previous frame determined to correspond to the feature region pixelsof the current frame.
 4. The method of claim 3, wherein: the determiningof the pixel values matches the feature region pixels of the at leastone previous frame, based upon a determined difference between thefeature region pixels of the current frame and pixel values ofcorresponding feature region pixels of the at least one previous framebeing a reference value or less, and wherein the reference value variesdepending on whether a feature region pixel of the current frame isdetermined to be included in a texture region or an edge region.
 5. Themethod of claim 2, wherein the classifying includes classifying, as thefeature region pixels, pixels of the current frame on which a trajectoryestimation is performed from feature region pixels of a feature regionof a previous frame.
 6. The method of claim 2, wherein the classifyingof the pixels or blocks of the current frame respectively into one ofthe feature region and the uniform region is controlled to maintain aminimal distance between the feature region pixels.
 7. The method ofclaim 6, wherein, when a first pixel of the current frame is classifiedas a feature region pixel, the classifying includes selectivelyclassifying second pixels of the current frame separated by at least theminimal distance from the first pixel into the feature region and theuniform region in accordance with a spatial spectrum of each secondpixel.
 8. The method of claim 7, wherein the classifying furthercomprises not classifying second pixels, which are not separated by atleast the minimal distance from the first pixel, into either the featureregion or the uniform region.
 9. The method of claim 2, furthercomprising: dividing the current frame into N×M-numbered blocks; andclassifying for each N×M-numbered block, as a feature region pixel, apixel having a greatest gradient value among pixels of each respectiveN×M-numbered block, with the greatest gradient value being equal to orgreater than a reference value.
 10. The method of claim 2, wherein theclassifying of the pixels or blocks of pixels of the current framerespectively into the feature region and the uniform region includesclassifying a pixel or block of pixels of the current frame having arelatively high spatial spectrum into the feature region, andclassifying a pixel or block of pixels of the current frame having arelatively lower spatial spectrum, less than the high spatial spectrum,into the uniform region.
 11. The method of claim 2, wherein theclassifying of the pixels or blocks of pixels of the current framerespectively into the feature region and the uniform region includesclassifying a pixel of the current frame into the feature region when adifference between a pixel value of the pixel of the current frame and apixel value of an adjacent pixel of the current frame is greater than orequal to a reference value, and classifying the pixel of the currentframe into the uniform region when the difference is less than thereference value.
 12. A method of producing a high definition video froma low definition video, the method comprising: determining featureregion pixels of an i-th frame of the low definition video by performingrespective trajectory estimation on feature region pixels of an (i−1)-thframe of the low definition video; determining pixel values of pixels ofa frame of the high definition video corresponding to the i-th frame bymatching the feature region pixels of the i-th frame with feature regionpixels of at least one previous frame corresponding to the featureregion pixels of the i-th frame; and determining, using interpolation,pixel values of the frame of the high definition video corresponding tothe i-th frame that are not determined by the matching, wherein thematching of the feature region pixels of the i-th frame with featureregion pixels of the at least one previous frame includes matching thefeature region pixels of the i-th frame with only feature region pixelsof the at least one previous frame, with the at least one previous frameincluding pixels in addition to the feature region pixels.
 13. Themethod of claim 12, wherein the feature region pixels of the (i−1)-thframe are pixels on which trajectory estimation has been performed fromfeature region pixels of an (i−2)-th frame of the low definition video,or pixels of the (i−1)-th frame having a relatively high spatialspectrum.
 14. The method of claim 13, wherein the pixels having therelatively high spatial spectrum are pixels having respective gradientvalues equal to or greater than a reference value.
 15. The method ofclaim 13, wherein each of the feature region pixels of the (i−1)-thframe are separated by at least a minimal distance.
 16. The method ofclaim 13, wherein the method further comprises: dividing the (i−1)-thframe into N×M-numbered blocks; and determining for each N×M-numberedblock, as one of the feature region pixels of the (i−1)-th frame, apixel having a greatest spatial spectrum, among plural pixels of eachrespective N×M-numbered block, that is equal to or greater than areference value.
 17. The method of claim 16, wherein, when a spatialspectrum of the pixel having the greatest spatial spectrum among pluralpixels of a respective N×M-numbered block is less than the referencevalue, the pixel is excluded from being a feature region pixel of the(i−1)-th frame.
 18. The method of claim 12, wherein the method furthercomprises: dividing the i-th frame into N×M-numbered blocks; anddetermining for each N×M-numbered block, as one of the feature regionpixels of the i-th frame, a pixel having a greatest spatial spectrum,among plural pixels of each respective N×M-numbered block, that is equalto or greater than a reference value.
 19. The method of claim 18,wherein, when a spatial spectrum of the pixel having the greatestspatial spectrum among plural pixels of a respective N×M-numbered blockis less than the reference value, all pixels of the respectiveN×M-numbered block, are excluded from being a feature region pixel ofthe i-th frame.
 20. The method of claim 12, further comprising: storingpixel values of the feature region pixels of the (i−1)-th frame andlocations of the feature region pixels of the (i−1)-th frame; andstoring pixel values of pixels neighboring the feature region pixels ofthe (i−1)-th frame and locations of the neighboring pixels.
 21. Themethod of claim 12, wherein the determining of the feature region pixelsof the i-th frame includes performing trajectory estimation on thefeature region pixels of the (i−1)-th frame by a sub-pixel unit of thei-th frame.
 22. An image processing method, the method comprising:receiving an image and trajectory information of the received image;individually classifying pixels or blocks of the received image into afeature region and a uniform region in accordance with a respectivespatial spectrum of each pixel of block, with pixels or blocksclassified into the feature region respectively being feature regionpixels or blocks; and producing a high definition image by interpolatingat least between pixels or blocks within the uniform region andinterpolating at least between pixels or blocks corresponding to thefeature region pixels based on the received trajectory information. 23.The method of claim 21, wherein the trajectory information of thereceived image is trajectory information for trajectory estimatingpixels from another image in a sequence of images including the receivedimage.
 24. An image processing method, the method comprising:individually classifying pixels or blocks of a first frame respectivelyinto one of the feature region and a uniform region of the first framein accordance with each respective spatial spectrum and storing pixelvalues and locations of pixels or blocks classified into the featureregion of the first frame in a registration memory, as feature regionpixels or blocks of the first frame; performing trajectory estimation onpixels or blocks of a second frame, subsequent to the first frame,including obtaining pixel values and locations of the feature regionpixels or blocks of the first frame from the registration memory, andidentifying trajectory estimated pixels or blocks of the second framebased on the obtained feature region pixels or blocks of the firstframe, and storing pixel values and locations of the trajectoryestimated pixels of blocks of the second frame in the registrationmemory; classifying the trajectory estimated pixels or blocks of thesecond frame into a feature region and individually classifying pixelsor blocks of the second frame, excluding the trajectory estimated pixelsor blocks of the second frame, respectively into one of the featureregion and a uniform region of the second frame in accordance with eachrespective spatial spectrum; matching feature region pixels or blocks ofthe feature region of the second frame with feature region pixels orblocks of the first frame, and projecting matched feature region pixelsor blocks of the first frame and the corresponding trajectory estimatedpixels or blocks of the second frame into a high definition frame havinga higher definition than the second frame; and interpolating pixels ofthe high definition frame, excluding the projected matched featureregion pixels or blocks of the first frame and the correspondingtrajectory estimated pixels or blocks of the second frame, based on thematched feature region pixels or blocks of the first frame and thecorresponding trajectory estimated pixels or blocks of the second frame,and interpolating pixels of the high definition frame based on pixels orblocks of the uniform region of the second region.
 25. At least onenon-transitory computer readable recording medium comprising computerreadable code to control at least one processing device to implement themethod of claim
 1. 26. At least one non-transitory computer readablerecording medium comprising computer readable code to control at leastone processing device to implement the method of claim
 2. 27. At leastone non-transitory computer readable recording medium comprisingcomputer readable code to control at least one processing device toimplement the method of claim
 12. 28. At least one non-transitorycomputer readable recording medium comprising computer readable code tocontrol at least one processing device to implement the method of claim22.
 29. At least one non-transitory computer readable recording mediumcomprising computer readable code to control at least one processingdevice to implement the method of claim
 24. 30. An image processingsystem, the system comprising: a trajectory estimation unit to performtrajectory estimation on pixels or blocks of a current frame, includingpixels or blocks corresponding to a feature region of the current frame,and classifying trajectory estimated pixels of blocks into the featureregion; a region classification unit to individually classify pixels orblocks of the current frame, excluding the trajectory estimated pixelsor blocks of the current frame, respectively into one of the featureregion and a uniform region in accordance with each respective spatialspectrum; a registration unit to match feature region pixels or blocksof the feature region of the current frame with feature region pixels orblocks of at least one previous frame, and to project matched featureregion pixels or blocks of the at least one previous frame and thecorresponding trajectory estimated pixels or blocks into a highdefinition frame having a higher definition than the current frame; anda high resolution interpolation unit to interpolate pixels or blocks ofthe high definition frame, excluding the projected matched featureregion pixels or blocks and the corresponding trajectory estimatedpixels or blocks, based on the matched feature region pixels or blocksand the corresponding trajectory estimated pixels or blocks, and basedon pixels or blocks of the uniform region.
 31. The system of claim 30,wherein the registration unit selectively matches the feature regionpixels of the at least one previous frame, based upon a determineddifference between the feature region pixels of the current frame andpixel values of corresponding feature region pixels of the at least oneprevious frame being a reference value or less.
 32. The system of claim31, wherein the registration unit further comprises; a textureregistration unit to selectively control the matching based upon adetermination that a pixel or block of the current frame represents atexture, where the reference value is a first reference value; an edgeregistration unit to selectively control the matching based upon adetermination that the pixel or block of the current frame represents anedge, and where the reference value is a second reference value lessthan the first reference value; and and a low pass filter to low passfilter results of the edge registration unit.
 33. An image processingsystem producing a high definition video from a low definition video,the system comprising: a region classification unit to individuallyclassify pixels or blocks of a current frame of the low definition videointo a feature region and a uniform region in accordance with arespective spatial spectrum of each pixel or block, with pixels orblocks classified into the feature region respectively being featureregion pixels or blocks; and a high resolution interpolation unit toproduce a high definition video corresponding to the feature regionbased on a plurality of frames of the low definition video with respectto the feature region and based on pixel value information from at leastone previous frame, and to produce a high definition video correspondingto the uniform region using interpolation at least between pixels orblocks of the current frame classified into the uniform region.
 34. Animage processing system producing a high definition video from a lowdefinition video, the system comprising: a trajectory estimation unit todetermine feature region pixels of an i-th frame of the low definitionvideo by performing respective trajectory estimation on feature regionpixels of an (i−1)-th frame of the low definition video; a registrationunit to determine pixel values of pixels of a frame of the highdefinition video corresponding to the i-th frame by matching the featureregion pixels of the i-th frame with feature region pixels of at leastone previous frame corresponding to the feature region pixels of thei-th frame and storing matched feature region pixels in a registrationmemory; and an interpolation unit to determine, using interpolation,pixel values of the frame of the high definition video corresponding tothe i-th frame that are not determined by the matching, wherein thematching of the feature region pixels of the i-th frame with featureregion pixels of the at least one previous frame includes matching thefeature region pixels of the i-th frame with only feature region pixelsof each of the at least one previous frame, with the at least oneprevious frame including pixels in addition to the feature regionpixels.
 35. The system of claim 34, wherein the feature region pixels ofthe (i−1)-th frame are pixels on which trajectory estimation has beenperformed from feature region pixels of an (i−2)-th frame of the lowdefinition video as indicated in the registration memory, or pixels ofthe (i−1)-th frame having a relatively high spatial spectrum.
 36. Thesystem of claim 35, wherein the trajectory estimated pixels of the i-thframe are pixels on which trajectory estimation has been performed fromfeature region pixels of the (i−1)-th frame.
 37. The system of claim 35,wherein the pixels having the relatively high spatial spectrum arepixels having respective gradient values equal to or greater than areference value.
 38. The system of claim 34, wherein each of the featureregion pixels of the i-th frame are separated by at least a minimaldistance.
 39. The system of claim 34, further comprising a regionclassification unit to divide the i-th frame into N×M-numbered blocks,and determine for each N×M-numbered block, as one of the feature regionpixels of the i-th frame, a pixel having a greatest spatial spectrum,among plural pixels of each respective N×M-numbered block, that is equalto or greater than a reference value.
 40. The system of claim 39,wherein, when a spatial spectrum of the pixel having the greatestspatial spectrum among plural pixels of a respective N×M-numbered blockis less than the reference value, the pixel is excluded from being afeature region pixel of the i-th frame.
 41. The system of claim 34,further comprising the registration memory, storing pixel values of thefeature region pixels of the (i−1)-th and (i−2)-th frames and locationsof the feature region pixels of the (i−1)-th and (i−2)-th frames, andstoring pixel values of pixels neighboring the feature region pixels ofthe (i−1)-th and (i−2)-th frames and locations of the neighboringpixels, wherein the registration unit performs the matching based uponmatching the feature region pixels of the i-th frame with feature regionpixels of the (i−1)-th and (i−2)-th frames corresponding to the featureregion pixels of the i-th frame, and the matching unit obtains pixelvalue and location information of the feature region pixels of the(i−1)-th and (i−2)-th frames from the registration memory.
 42. Thesystem of claim 34, wherein the trajectory estimation unit determinesthe feature region pixels of the i-th frame by performing trajectoryestimation on the feature region pixels of the (i−1)-th frame by asub-pixel unit of the i-th frame.